1
|
Sharma A, Verhaak PF, McCoy TH, Perlis RH, Doshi-Velez F. Identifying data-driven subtypes of major depressive disorder with electronic health records. J Affect Disord 2024; 356:64-70. [PMID: 38565338 DOI: 10.1016/j.jad.2024.03.162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration. METHODS We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups. RESULTS Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %). LIMITATIONS Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions. CONCLUSION These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.
Collapse
Affiliation(s)
- Abhishek Sharma
- Harvard John A. Paulson School of Engineering and Applied Sciences, 29 Oxford Street, Cambridge, MA 02138, United States of America
| | - Pilar F Verhaak
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, United States of America
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, United States of America; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States of America
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114, United States of America; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States of America.
| | - Finale Doshi-Velez
- Harvard John A. Paulson School of Engineering and Applied Sciences, 29 Oxford Street, Cambridge, MA 02138, United States of America.
| |
Collapse
|
2
|
Jiao P, Chen H, Tang H, Bao Q, Zhang L, Zhao Z, Wu H. Contrastive representation learning on dynamic networks. Neural Netw 2024; 174:106240. [PMID: 38521019 DOI: 10.1016/j.neunet.2024.106240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 03/11/2024] [Accepted: 03/13/2024] [Indexed: 03/25/2024]
Abstract
Representation learning for dynamic networks is designed to learn the low-dimensional embeddings of nodes that can well preserve the snapshot structure, properties and temporal evolution of dynamic networks. However, current dynamic network representation learning methods tend to focus on estimating or generating observed snapshot structures, paying excessive attention to network details, and disregarding distinctions between snapshots with larger time intervals, resulting in less robustness for sparse or noisy networks. To alleviate these challenges, this paper proposes a contrastive mechanism for temporal representation learning on dynamic networks, inspired by the success of contrastive learning in visual and static network representation learning. This paper proposes a novel Dynamic Network Contrastive representation Learning (DNCL) model. Specifically, contrast objective functions are constructed using intra-snapshot and inter-snapshot contrasts to capture the network topology, node feature information, and network evolution information, respectively. Rather than estimating or generating ground-truth network features, the proposed approach maximizes mutual information between nodes from different time steps and views generated. The experimental results of link prediction, node classification, and clustering on several real-world and synthetic networks demonstrate the superiority of DNCL over state-of-the-art methods, indicating the effectiveness of the proposed approach for dynamic network representation learning.
Collapse
Affiliation(s)
- Pengfei Jiao
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China; Data Security Governance Zhejiang Engineering Research Center, Hangzhou, 310018, China
| | - Hongjiang Chen
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Huijun Tang
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Qing Bao
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Long Zhang
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
| | - Zhidong Zhao
- School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China; Data Security Governance Zhejiang Engineering Research Center, Hangzhou, 310018, China.
| | - Huaming Wu
- Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China.
| |
Collapse
|
3
|
Shen Z, Qiu Y, Liu J, He L, Lin Z. Efficient learning of Scale-Adaptive Nearly Affine Invariant Networks. Neural Netw 2024; 174:106229. [PMID: 38490114 DOI: 10.1016/j.neunet.2024.106229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 03/04/2024] [Accepted: 03/07/2024] [Indexed: 03/17/2024]
Abstract
Recent research has demonstrated the significance of incorporating invariance into neural networks. However, existing methods require direct sampling over the entire transformation set, notably computationally taxing for large groups like the affine group. In this study, we propose a more efficient approach by addressing the invariances of the subgroups within a larger group. For tackling affine invariance, we split it into the Euclidean group E(n) and uni-axial scaling group US(n), handling invariance individually. We employ an E(n)-invariant model for E(n)-invariance and average model outputs over data augmented from a US(n) distribution for US(n)-invariance. Our method maintains a favorable computational complexity of O(N2) in 2D and O(N4) in 3D scenarios, in contrast to the O(N6) (2D) and O(N12) (3D) complexities of averaged models. Crucially, the scale range for augmentation adapts during training to avoid excessive scale invariance. This is the first time nearly exact affine invariance is incorporated into neural networks without directly sampling the entire group. Extensive experiments unequivocally confirm its superiority, achieving new state-of-the-art results in affNIST and SIM2MNIST classifications while consuming less than 15% of inference time and fewer computational resources and model parameters compared to averaged models.
Collapse
Affiliation(s)
| | - Yeqing Qiu
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | | | - Lingshen He
- National Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University, Beijing, 100871, China
| | - Zhouchen Lin
- National Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University, Beijing, 100871, China; Institute for Artificial Intelligence, Peking University, Peking, 100871, China; Peng Cheng Laboratory, Shenzhen, 518000, China.
| |
Collapse
|
4
|
Shi M, Tian Y, Luo Y, Elze T, Wang M. RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps. Med Image Anal 2024; 94:103110. [PMID: 38458093 DOI: 10.1016/j.media.2024.103110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/09/2024] [Accepted: 02/15/2024] [Indexed: 03/10/2024]
Abstract
Optical coherence tomography imaging provides a crucial clinical measurement for diagnosing and monitoring glaucoma through the two-dimensional retinal nerve fiber layer (RNFL) thickness (RNFLT) map. Researchers have been increasingly using neural models to extract meaningful features from the RNFLT map, aiming to identify biomarkers for glaucoma and its progression. However, accurately representing the RNFLT map features relevant to glaucoma is challenging due to significant variations in retinal anatomy among individuals, which confound the pathological thinning of the RNFL. Moreover, the presence of artifacts in the RNFLT map, caused by segmentation errors in the context of degraded image quality and defective imaging procedures, further complicates the task. In this paper, we propose a general framework called RNFLT2Vec for unsupervised learning of vectorized feature representations from RNFLT maps. Our method includes an artifact correction component that learns to rectify RNFLT values at artifact locations, producing a representation reflecting the RNFLT map without artifacts. Additionally, we incorporate two regularization techniques to encourage discriminative representation learning. Firstly, we introduce a contrastive learning-based regularization to capture the similarities and dissimilarities between RNFLT maps. Secondly, we employ a consistency learning-based regularization to align pairwise distances of RNFLT maps with their corresponding thickness distributions. Through extensive experiments on a large-scale real-world dataset, we demonstrate the superiority of RNFLT2Vec in three different clinical tasks: RNFLT pattern discovery, glaucoma detection, and visual field prediction. Our results validate the effectiveness of our framework and its potential to contribute to a better understanding and diagnosis of glaucoma.
Collapse
Affiliation(s)
- Min Shi
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Yu Tian
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Yan Luo
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Tobias Elze
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Mengyu Wang
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
5
|
Wang Z, Chen J, Gong M, Shao Z. Higher-order neurodynamical equation for simplex prediction. Neural Netw 2024; 173:106185. [PMID: 38387202 DOI: 10.1016/j.neunet.2024.106185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/01/2024] [Accepted: 02/15/2024] [Indexed: 02/24/2024]
Abstract
It is demonstrated that higher-order patterns beyond pairwise relations can significantly enhance the learning capability of existing graph-based models, and simplex is one of the primary form for graphically representing higher-order patterns. Predicting unknown (disappeared) simplices in real-world complex networks can provide us with deeper insights, thereby assisting us in making better decisions. Nevertheless, previous efforts to predict simplices suffer from two issues: (i) they mainly focus on 2- or 3-simplices, and there are few models available for predicting simplices of arbitrary orders, and (ii) they lack the ability to analyze and learn the features of simplices from the perspective of dynamics. In this paper, we present a Higher-order Neurodynamical Equation for Simplex Prediction of arbitrary order (HNESP), which is a framework that combines neural networks and neurodynamics. Specifically, HNESP simulates the dynamical coupling process of nodes in simplicial complexes through different relations (i.e., strong pairwise relation, weak pairwise relation, and simplex) to learn node-level representations, while explaining the learning mechanism of neural networks from neurodynamics. To enrich the higher-order information contained in simplices, we exploit the entropy and normalized multivariate mutual information of different sub-structures of simplices to acquire simplex-level representations. Furthermore, simplex-level representations and multi-layer perceptron are used to quantify the existence probability of simplices. The effectiveness of HNESP is demonstrated by extensive simulations on seven higher-order benchmarks. Experimental results show that HNESP improves the AUC values of the state-of-the-art baselines by an average of 8.32%. Our implementations will be publicly available at: https://github.com/jianruichen/HNESP.
Collapse
Affiliation(s)
- Zhihui Wang
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi'an, China; School of Computer Science, Shaanxi Normal University, Xi'an, China.
| | - Jianrui Chen
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi'an, China; School of Computer Science, Shaanxi Normal University, Xi'an, China.
| | - Maoguo Gong
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi'an, China; School of Electronic Engineering, Xidian University, Xi'an, China.
| | - Zhongshi Shao
- Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi'an, China; School of Computer Science, Shaanxi Normal University, Xi'an, China.
| |
Collapse
|
6
|
Zhou Y, Zhu C, Zhu W, Li H. SCMEA: A stacked co-enhanced model for entity alignment based on multi-aspect information fusion and bidirectional contrastive learning. Neural Netw 2024; 173:106178. [PMID: 38367354 DOI: 10.1016/j.neunet.2024.106178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 10/31/2023] [Accepted: 02/13/2024] [Indexed: 02/19/2024]
Abstract
Entity alignment refers to discovering the entity pairs with the same realistic meaning in different knowledge graphs. This technology is of great significance for completing and fusing knowledge graphs. Recently, methods based on knowledge representation learning have achieved remarkable achievements in entity alignment. However, most existing approaches do not mine hidden information in the knowledge graph as much as possible. This paper suggests SCMEA, a novel cross-lingual entity alignment framework based on multi-aspect information fusion and bidirectional contrastive learning. SCMEA initially adopts diverse representation learning models to embed multi-aspect information of entities and integrates them into a unified embedding space with an adaptive weighted mechanism to overcome the missing information and the problem of different-aspect information are not uniform. Then, we propose a stacked relation-entity co-enhanced model to further improve the representations of entities, wherein relation representation is modeled using an Entity Collector with Global Entity Attention. Finally, a combined loss function based on improved bidirectional contrastive learning is introduced to optimize model parameters and entity representation, effectively mitigating the hubness problem and accelerating model convergence. We conduct extensive experiments to evaluate the alignment performance of SCMEA. The overall experimental results, ablation studies, and analysis performed on five cross-lingual datasets demonstrate that our model achieves varying degrees of performance improvement and verifies the effectiveness and robustness of the model.
Collapse
Affiliation(s)
- Yunfeng Zhou
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100020, China.
| | - Cui Zhu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100020, China.
| | - Wenjun Zhu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100020, China.
| | - Hongyang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100020, China.
| |
Collapse
|
7
|
Amiranashvili T, Lüdke D, Li HB, Zachow S, Menze BH. Learning continuous shape priors from sparse data with neural implicit functions. Med Image Anal 2024; 94:103099. [PMID: 38395009 DOI: 10.1016/j.media.2024.103099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 10/31/2023] [Accepted: 01/30/2024] [Indexed: 02/25/2024]
Abstract
Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.g., to reduce radiation exposure in CT or image acquisition time in MR imaging. For existing shape modeling approaches, the resolution of the emerging model is limited to the resolution of the training shapes. Therefore, any missing information between slices prohibits existing methods from learning a high-resolution shape prior. We propose a novel shape modeling approach that can be trained on sparse, binary segmentation masks with large slice distances. This is achieved through employing continuous shape representations based on neural implicit functions. After training, our model can reconstruct shapes from various sparse inputs at high target resolutions beyond the resolution of individual training examples. We successfully reconstruct high-resolution shapes from as few as three orthogonal slices. Furthermore, our shape model allows us to embed various sparse segmentation masks into a common, low-dimensional latent space - independent of the acquisition direction, resolution, spacing, and field of view. We show that the emerging latent representation discriminates between healthy and pathological shapes, even when provided with sparse segmentation masks. Lastly, we qualitatively demonstrate that the emerging latent space is smooth and captures characteristic modes of shape variation. We evaluate our shape model on two anatomical structures: the lumbar vertebra and the distal femur, both from publicly available datasets.
Collapse
Affiliation(s)
- Tamaz Amiranashvili
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Department of Computer Science, Technical University of Munich, Munich, Germany.
| | - David Lüdke
- Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany; Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Hongwei Bran Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Stefan Zachow
- Visual and Data-Centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Bjoern H Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Department of Computer Science, Technical University of Munich, Munich, Germany
| |
Collapse
|
8
|
Csiszárik A, Kiss MF, Kőrösi-Szabó P, Muntag M, Papp G, Varga D. Mode combinability: Exploring convex combinations of permutation aligned models. Neural Netw 2024; 173:106204. [PMID: 38412738 DOI: 10.1016/j.neunet.2024.106204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/28/2023] [Accepted: 02/20/2024] [Indexed: 02/29/2024]
Abstract
We explore element-wise convex combinations of two permutation-aligned neural network parameter vectors ΘA and ΘB of size d. We conduct extensive experiments by examining various distributions of such model combinations parametrized by elements of the hypercube [0,1]d and its vicinity. Our findings reveal that broad regions of the hypercube form surfaces of low loss values, indicating that the notion of linear mode connectivity extends to a more general phenomenon which we call mode combinability. We also make several novel observations regarding linear mode connectivity and model re-basin. We demonstrate a transitivity property: two models re-based to a common third model are also linear mode connected, and a robustness property: even with significant perturbations of the neuron matchings the resulting combinations continue to form a working model. Moreover, we analyze the functional and weight similarity of model combinations and show that such combinations are non-vacuous in the sense that there are significant functional differences between the resulting models.
Collapse
Affiliation(s)
- Adrián Csiszárik
- HUN-REN Alfréd Rényi Institute of Mathematics, Reáltanoda utca 13-15., Budapest, 1053, Hungary; Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, 1117, Hungary.
| | - Melinda F Kiss
- HUN-REN Alfréd Rényi Institute of Mathematics, Reáltanoda utca 13-15., Budapest, 1053, Hungary; Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, 1117, Hungary.
| | - Péter Kőrösi-Szabó
- HUN-REN Alfréd Rényi Institute of Mathematics, Reáltanoda utca 13-15., Budapest, 1053, Hungary.
| | - Márton Muntag
- HUN-REN Alfréd Rényi Institute of Mathematics, Reáltanoda utca 13-15., Budapest, 1053, Hungary.
| | - Gergely Papp
- HUN-REN Alfréd Rényi Institute of Mathematics, Reáltanoda utca 13-15., Budapest, 1053, Hungary.
| | - Dániel Varga
- HUN-REN Alfréd Rényi Institute of Mathematics, Reáltanoda utca 13-15., Budapest, 1053, Hungary.
| |
Collapse
|
9
|
Luo Q, Xu TB, Liu F, Li T, Wei Z. Learning shared template representation with augmented feature for multi-object pose estimation. Neural Netw 2024; 176:106352. [PMID: 38713968 DOI: 10.1016/j.neunet.2024.106352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 03/31/2024] [Accepted: 04/28/2024] [Indexed: 05/09/2024]
Abstract
Template matching pose estimation methods based on deep learning have made significant advancements via metric learning or reconstruction learning. Existing approaches primarily build distinct template representation libraries (codebooks) from rendered images for each object, which complicate the training process and increase memory cost for multi-object tasks. Additionally, they struggle to effectively handle discrepancies between the distributions of training and test sets, particularly for occluded objects, resulting in suboptimal matching accuracy. In this study, we propose a shared template representation learning method with augmented semantic features to address these issues. Our method learns representations concurrently using metric and reconstruction learning as similarity constraints, and augments response of network to objects through semantic feature constraints for better generalization performance. Furthermore, rotation matrices serve as templates for codebook construction, leading to excellent matching accuracy compared to rendered images. Notably, it contributes to the effective decoupling of object categories and templates, necessitating the maintenance of only a shared codebook in multi-object pose estimation tasks. Extensive experiments on Linemod, Linemod-Occluded and TLESS datasets demonstrate that the proposed method employing shared templates achieves superior matching accuracy. Moreover, proposed method exhibits robustness on a collected aircraft dataset, further validating its efficacy.
Collapse
Affiliation(s)
- Qifeng Luo
- The Ministry of Education Key Laboratory of Precision Opto-Mechatronics Technology, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
| | - Ting-Bing Xu
- The Ministry of Education Key Laboratory of Precision Opto-Mechatronics Technology, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China; SenseTime Group Limited, Beijing, 100191, China.
| | - Fulin Liu
- The Ministry of Education Key Laboratory of Precision Opto-Mechatronics Technology, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
| | - Tianren Li
- The Ministry of Education Key Laboratory of Precision Opto-Mechatronics Technology, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
| | - Zhenzhong Wei
- The Ministry of Education Key Laboratory of Precision Opto-Mechatronics Technology, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, 100191, China.
| |
Collapse
|
10
|
An S, Oh TJ, Kim SW, Jung JJ. Self-clustered GAN for precipitation nowcasting. Sci Rep 2024; 14:9755. [PMID: 38679623 PMCID: PMC11056384 DOI: 10.1038/s41598-024-60253-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/20/2024] [Indexed: 05/01/2024] Open
Abstract
This paper proposes a novel GAN framework with self-clustering approach for precipitation nowcasting (ClusterCast). Previous studies have primarily captured the motion vector using only a single latent space, making the models difficult to adapt to disparate space-time distribution of precipitation. Environmental factors (e.g., regional characteristics and precipitation scale) have an impact on precipitation systems and can cause non-stationary distribution. To tackle this problem, our key idea is to train a generator network to predict future radar frames by learning a sub-network that automatically labels precipitation types from a generative model. The training process consists of (i) clustering the hierarchical features derived from the generator stem using a sub-network and (ii) predicting future radar frames according to the self-supervised labels, enabling heterogeneous latent representation. Additionally, we attempt an ensemble forecast that prescribes random perturbations to improve performance. With the flexibility of representation learning, ClusterCast enables the model to learn precipitation distribution more accurately. Results indicate that our method generates non-blurry future frames by preventing mode collapse, and the proposed method demonstrates robustness across various precipitation scenarios. Extensive experiments demonstrate that our method outperforms four benchmarks on a 2-h prediction basis with a mean squared error (MSE) of 8.9% on unseen datasets.
Collapse
Affiliation(s)
- Sojung An
- Korea Institute of Atmospheric Prediction Systems, Seoul, 07071, Republic of Korea.
| | - Tae-Jin Oh
- Korea Institute of Atmospheric Prediction Systems, Seoul, 07071, Republic of Korea
| | - Sang-Wook Kim
- Korea Institute of Atmospheric Prediction Systems, Seoul, 07071, Republic of Korea
- CJ Cheiljedang, Seoul, 04637, Republic of Korea
| | - Jason J Jung
- Chung-Ang University, Seoul, 06974, Republic of Korea
| |
Collapse
|
11
|
You B, Liu H. Multimodal information bottleneck for deep reinforcement learning with multiple sensors. Neural Netw 2024; 176:106347. [PMID: 38688069 DOI: 10.1016/j.neunet.2024.106347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/17/2024] [Accepted: 04/25/2024] [Indexed: 05/02/2024]
Abstract
Reinforcement learning has achieved promising results on robotic control tasks but struggles to leverage information effectively from multiple sensory modalities that differ in many characteristics. Recent works construct auxiliary losses based on reconstruction or mutual information to extract joint representations from multiple sensory inputs to improve the sample efficiency and performance of reinforcement learning algorithms. However, the representations learned by these methods could capture information irrelevant to learning a policy and may degrade the performance. We argue that compressing information in the learned joint representations about raw multimodal observations is helpful, and propose a multimodal information bottleneck model to learn task-relevant joint representations from egocentric images and proprioception. Our model compresses and retains the predictive information in multimodal observations for learning a compressed joint representation, which fuses complementary information from visual and proprioceptive feedback and meanwhile filters out task-irrelevant information in raw multimodal observations. We propose to minimize the upper bound of our multimodal information bottleneck objective for computationally tractable optimization. Experimental evaluations on several challenging locomotion tasks with egocentric images and proprioception show that our method achieves better sample efficiency and zero-shot robustness to unseen white noise than leading baselines. We also empirically demonstrate that leveraging information from egocentric images and proprioception is more helpful for learning policies on locomotion tasks than solely using one single modality.
Collapse
Affiliation(s)
- Bang You
- Department of Computer Science and Technology, Beijing National Research Centre for Information Science and Technology, Tsinghua University, Beijing 100084, China
| | - Huaping Liu
- Department of Computer Science and Technology, Beijing National Research Centre for Information Science and Technology, Tsinghua University, Beijing 100084, China.
| |
Collapse
|
12
|
VanBerlo B, Hoey J, Wong A. A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound. BMC Med Imaging 2024; 24:79. [PMID: 38580932 PMCID: PMC10998380 DOI: 10.1186/s12880-024-01253-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 03/18/2024] [Indexed: 04/07/2024] Open
Abstract
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.
Collapse
Affiliation(s)
- Blake VanBerlo
- Cheriton School of Computer Science, 200 University Ave W, N2L 3G1, Waterloo, Canada.
| | - Jesse Hoey
- Cheriton School of Computer Science, 200 University Ave W, N2L 3G1, Waterloo, Canada
| | - Alexander Wong
- Department of Systems Design Engineering, 200 University Ave W, N2L 3G1, Waterloo, Canada
| |
Collapse
|
13
|
Hartog PBR, Krüger F, Genheden S, Tetko IV. Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition. J Cheminform 2024; 16:39. [PMID: 38576047 PMCID: PMC10993590 DOI: 10.1186/s13321-024-00824-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/09/2024] [Indexed: 04/06/2024] Open
Abstract
Stakeholders of machine learning models desire explainable artificial intelligence (XAI) to produce human-understandable and consistent interpretations. In computational toxicity, augmentation of text-based molecular representations has been used successfully for transfer learning on downstream tasks. Augmentations of molecular representations can also be used at inference to compare differences between multiple representations of the same ground-truth. In this study, we investigate the robustness of eight XAI methods using test-time augmentation for a molecular-representation model in the field of computational toxicity prediction. We report significant differences between explanations for different representations of the same ground-truth, and show that randomized models have similar variance. We hypothesize that text-based molecular representations in this and past research reflect tokenization more than learned parameters. Furthermore, we see a greater variance between in-domain predictions than out-of-domain predictions, indicating XAI measures something other than learned parameters. Finally, we investigate the relative importance given to expert-derived structural alerts and find similar importance given irregardless of applicability domain, randomization and varying training procedures. We therefore caution future research to validate their methods using a similar comparison to human intuition without further investigation. SCIENTIFIC CONTRIBUTION: In this research we critically investigate XAI through test-time augmentation, contrasting previous assumptions about using expert validation and showing inconsistencies within models for identical representations. SMILES augmentation has been used to increase model accuracy, but was here adapted from the field of image test-time augmentation to be used as an independent indication of the consistency within SMILES-based molecular representation models.
Collapse
Affiliation(s)
- Peter B R Hartog
- Molecular AI, Discovery Sciences, R &D, AstraZeneca, 431 83, Mölndal, Sweden.
- Institute of Structural Biology, Helmholtz Munich, Munich, 85764, Germany.
| | - Fabian Krüger
- Institute of Structural Biology, Helmholtz Munich, Munich, 85764, Germany
| | - Samuel Genheden
- Molecular AI, Discovery Sciences, R &D, AstraZeneca, 431 83, Mölndal, Sweden
| | - Igor V Tetko
- Institute of Structural Biology, Helmholtz Munich, Munich, 85764, Germany
| |
Collapse
|
14
|
Hojjati H, Ho TKK, Armanfard N. Self-supervised anomaly detection in computer vision and beyond: A survey and outlook. Neural Netw 2024; 172:106106. [PMID: 38232432 DOI: 10.1016/j.neunet.2024.106106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 01/19/2024]
Abstract
Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity, finance, and healthcare, by identifying patterns or events that deviate from normal behavior. In recent years, significant progress has been made in this field due to the remarkable growth of deep learning models. Notably, the advent of self-supervised learning has sparked the development of novel AD algorithms that outperform the existing state-of-the-art approaches by a considerable margin. This paper aims to provide a comprehensive review of the current methodologies in self-supervised anomaly detection. We present technical details of the standard methods and discuss their strengths and drawbacks. We also compare the performance of these models against each other and other state-of-the-art anomaly detection models. Finally, the paper concludes with a discussion of future directions for self-supervised anomaly detection, including the development of more effective and efficient algorithms and the integration of these techniques with other related fields, such as multi-modal learning.
Collapse
Affiliation(s)
- Hadi Hojjati
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada; Mila - Quebec AI Institute, Montreal, QC, Canada.
| | - Thi Kieu Khanh Ho
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada; Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Narges Armanfard
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada; Mila - Quebec AI Institute, Montreal, QC, Canada
| |
Collapse
|
15
|
Liu A, Borisyuk A. Investigating navigation strategies in the Morris Water Maze through deep reinforcement learning. Neural Netw 2024; 172:106050. [PMID: 38232429 DOI: 10.1016/j.neunet.2023.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 11/09/2023] [Accepted: 12/01/2023] [Indexed: 01/19/2024]
Abstract
Navigation is a complex skill with a long history of research in animals and humans. In this work, we simulate the Morris Water Maze in 2D to train deep reinforcement learning agents. We perform automatic classification of navigation strategies, analyze the distribution of strategies used by artificial agents, and compare them with experimental data to show similar learning dynamics as those seen in humans and rodents. We develop environment-specific auxiliary tasks and examine factors affecting their usefulness. We suggest that the most beneficial tasks are potentially more biologically feasible for real agents to use. Lastly, we explore the development of internal representations in the activations of artificial agent neural networks. These representations resemble place cells and head-direction cells found in mouse brains, and their presence has correlation to the navigation strategies that artificial agents employ.
Collapse
Affiliation(s)
- Andrew Liu
- Department of Mathematics, 155 E 1400 S, Salt Lake City, UT 84109, USA.
| | - Alla Borisyuk
- Department of Mathematics, 155 E 1400 S, Salt Lake City, UT 84109, USA.
| |
Collapse
|
16
|
Kyung S, Jang M, Park S, Yoon HM, Hong GS, Kim N. Supervised representation learning based on various levels of pediatric radiographic views for transfer learning. Sci Rep 2024; 14:7551. [PMID: 38555414 PMCID: PMC10981659 DOI: 10.1038/s41598-024-58163-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/26/2024] [Indexed: 04/02/2024] Open
Abstract
Transfer learning plays a pivotal role in addressing the paucity of data, expediting training processes, and enhancing model performance. Nonetheless, the prevailing practice of transfer learning predominantly relies on pre-trained models designed for the natural image domain, which may not be well-suited for the medical image domain in grayscale. Recognizing the significance of leveraging transfer learning in medical research, we undertook the construction of class-balanced pediatric radiograph datasets collectively referred to as PedXnets, grounded in radiographic views using the pediatric radiographs collected over 24 years at Asan Medical Center. For PedXnets pre-training, approximately 70,000 X-ray images were utilized. Three different pre-training weights of PedXnet were constructed using Inception V3 for various radiation perspective classifications: Model-PedXnet-7C, Model-PedXnet-30C, and Model-PedXnet-68C. We validated the transferability and positive effects of transfer learning of PedXnets through pediatric downstream tasks including fracture classification and bone age assessment (BAA). The evaluation of transfer learning effects through classification and regression metrics showed superior performance of Model-PedXnets in quantitative assessments. Additionally, visual analyses confirmed that the Model-PedXnets were more focused on meaningful regions of interest.
Collapse
Affiliation(s)
- Sunggu Kyung
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea
| | - Miso Jang
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Seungju Park
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Hee Mang Yoon
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Gil-Sun Hong
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-gu, Seoul, 05505, Republic of Korea.
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-gu, Seoul, 05505, Republic of Korea.
| |
Collapse
|
17
|
Hunklinger A, Hartog P, Šícho M, Godin G, Tetko IV. The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge. SLAS Discov 2024; 29:100144. [PMID: 38316342 DOI: 10.1016/j.slasd.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 01/06/2024] [Accepted: 01/22/2024] [Indexed: 02/07/2024]
Abstract
The EUOS/SLAS challenge aimed to facilitate the development of reliable algorithms to predict the aqueous solubility of small molecules using experimental data from 100 K compounds. In total, hundred teams took part in the challenge to predict low, medium and highly soluble compounds as measured by the nephelometry assay. This article describes the winning model, which was developed using the publicly available Online CHEmical database and Modeling environment (OCHEM) available on the website https://ochem.eu/article/27. We describe in detail the assumptions and steps used to select methods, descriptors and strategy which contributed to the winning solution. In particular we show that consensus based on 28 models calculated using descriptor-based and representation learning methods allowed us to obtain the best score, which was higher than those based on individual approaches or consensus models developed using each individual approach. A combination of diverse models allowed us to decrease both bias and variance of individual models and to calculate the highest score. The model based on Transformer CNN contributed the best individual score thus highlighting the power of Natural Language Processing (NLP) methods. The inclusion of information about aleatoric uncertainty would be important to better understand and use the challenge data by the contestants.
Collapse
Affiliation(s)
- Andrea Hunklinger
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), DE-85764 Neuherberg, Germany
| | - Peter Hartog
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), DE-85764 Neuherberg, Germany
| | - Martin Šícho
- Leiden Academic Centre for Drug Research, Leiden University, 55 Einsteinweg, 2333 CC Leiden, the Netherlands; CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - Guillaume Godin
- dsm-firmenich SA, Rue de la Bergère 7, CH-1242 Satigny, Switzerland
| | - Igor V Tetko
- Institute of Structural Biology, Molecular Targets and Therapeutics Center, Helmholtz Munich-Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), DE-85764 Neuherberg, Germany; BIGCHEM GmbH, Valerystr. 49, DE-85716 Unterschleißheim, Germany.
| |
Collapse
|
18
|
Li J, Sun Q, Zhang F, Yang B. Meta-structure-based graph attention networks. Neural Netw 2024; 171:362-373. [PMID: 38134599 DOI: 10.1016/j.neunet.2023.12.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 10/26/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023]
Abstract
Due to the ubiquity of graph-structured data, Graph Neural Network (GNN) have been widely used in different tasks and domains and good results have been achieved in tasks such as node classification and link prediction. However, there are still many challenges in representation learning of heterogeneous networks. Existing graph neural network models are partly based on homogeneous graphs, which do not take into account the rich semantic information of nodes and edges due to their different types; And partly based on heterogeneous graphs, which require predefined meta-structures (include meta-paths and meta-graphs) and do not take into account the different effects of different meta-structures on node representation. In this paper, we propose the MS-GAN model, which consists of four parts: graph structure learner, graph structure expander, graph structure filter and graph structure parser. The graph structure learner automatically generates a graph structure consisting of useful meta-paths by selecting and combining the sub-adjacent matrices in the original graph using a 1 × 1 convolution. The graph structure expander further generates a graph structure containing meta-graphs by Hadamard product based on the previous step. The graph structure filterer filters out graph structures that are more effective for downstream classification tasks based on diversity. The graph structure parser assigns different weights to graph structures consisting of different meta-structures by a semantic hierarchical attention. Finally, through experiments on four datasets and meta-structure visualization analysis, it is shown that MS-GAN can automatically generate useful meta-structures and assign different weights to different meta-structures.
Collapse
Affiliation(s)
- Jin Li
- Harbin Engineering University, Harbin, 150001, Hei Longjiang, China
| | - Qingyu Sun
- Harbin Engineering University, Harbin, 150001, Hei Longjiang, China.
| | - Feng Zhang
- Harbin Engineering University, Harbin, 150001, Hei Longjiang, China
| | | |
Collapse
|
19
|
Yang S, Cui L, Wang L, Wang T, You J. Enhancing multimodal depression diagnosis through representation learning and knowledge transfer. Heliyon 2024; 10:e25959. [PMID: 38380046 PMCID: PMC10877283 DOI: 10.1016/j.heliyon.2024.e25959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 02/22/2024] Open
Abstract
Depression is a complex mental health disorder that presents significant challenges in diagnosis and treatment. This study proposes an innovative approach, leveraging artificial intelligence advancements, to enhance multimodal depression diagnosis. The diagnosis of depression often relies on subjective assessments and clinical interviews, leading to potential biases and inaccuracies. Additionally, integrating diverse data modalities, such as textual, imaging, and audio information, poses technical challenges due to data heterogeneity and high dimensionality. To address these challenges, this paper proposes the RLKT-MDD (Representation Learning and Knowledge Transfer for Multimodal Depression Diagnosis) model framework. Representation learning enables the model to autonomously discover meaningful patterns and features from diverse data sources, surpassing traditional feature engineering methods. Knowledge transfer facilitates the effective transfer of knowledge from related domains, improving the model's performance in depression diagnosis. Furthermore, we analyzed the interpretability of the representation learning process, enhancing the transparency and trustworthiness of the diagnostic process. We extensively experimented with the DAIC-WOZ dataset, a diverse collection of multimodal data from clinical settings, to evaluate our proposed approach. The results demonstrate promising outcomes, indicating significant improvements over conventional diagnostic methods. Our study provides valuable insights into cutting-edge techniques for depression diagnosis, enabling more effective and personalized mental health interventions.
Collapse
Affiliation(s)
- Shanliang Yang
- School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
| | - Lichao Cui
- School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
| | - Lei Wang
- School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
| | - Tao Wang
- School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
| | - Jiebing You
- Department of Neurology, Zibo Central Hospital, Zibo, 255036, China
| |
Collapse
|
20
|
Shiri I, Razeghi B, Ferdowsi S, Salimi Y, Gündüz D, Teodoro D, Voloshynovskiy S, Zaidi H. PRIMIS: Privacy-preserving medical image sharing via deep sparsifying transform learning with obfuscation. J Biomed Inform 2024; 150:104583. [PMID: 38191010 DOI: 10.1016/j.jbi.2024.104583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 11/19/2023] [Accepted: 01/02/2024] [Indexed: 01/10/2024]
Abstract
OBJECTIVE The primary objective of our study is to address the challenge of confidentially sharing medical images across different centers. This is often a critical necessity in both clinical and research environments, yet restrictions typically exist due to privacy concerns. Our aim is to design a privacy-preserving data-sharing mechanism that allows medical images to be stored as encoded and obfuscated representations in the public domain without revealing any useful or recoverable content from the images. In tandem, we aim to provide authorized users with compact private keys that could be used to reconstruct the corresponding images. METHOD Our approach involves utilizing a neural auto-encoder. The convolutional filter outputs are passed through sparsifying transformations to produce multiple compact codes. Each code is responsible for reconstructing different attributes of the image. The key privacy-preserving element in this process is obfuscation through the use of specific pseudo-random noise. When applied to the codes, it becomes computationally infeasible for an attacker to guess the correct representation for all the codes, thereby preserving the privacy of the images. RESULTS The proposed framework was implemented and evaluated using chest X-ray images for different medical image analysis tasks, including classification, segmentation, and texture analysis. Additionally, we thoroughly assessed the robustness of our method against various attacks using both supervised and unsupervised algorithms. CONCLUSION This study provides a novel, optimized, and privacy-assured data-sharing mechanism for medical images, enabling multi-party sharing in a secure manner. While we have demonstrated its effectiveness with chest X-ray images, the mechanism can be utilized in other medical images modalities as well.
Collapse
Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Behrooz Razeghi
- Department of Computer Science, University of Geneva, Switzerland; Idiap Research Institute, Switzerland
| | - Sohrab Ferdowsi
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Deniz Gündüz
- Department of Electrical and Electronic Engineering, Imperial College London, UK
| | - Douglas Teodoro
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Denmark; University Research and Innovation Center, Óbuda University, Budapest, Hungary.
| |
Collapse
|
21
|
Kpanou R, Dallaire P, Rousseau E, Corbeil J. Learning self-supervised molecular representations for drug-drug interaction prediction. BMC Bioinformatics 2024; 25:47. [PMID: 38291362 PMCID: PMC10829170 DOI: 10.1186/s12859-024-05643-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024] Open
Abstract
Drug-drug interactions (DDI) are a critical concern in healthcare due to their potential to cause adverse effects and compromise patient safety. Supervised machine learning models for DDI prediction need to be optimized to learn abstract, transferable features, and generalize to larger chemical spaces, primarily due to the scarcity of high-quality labeled DDI data. Inspired by recent advances in computer vision, we present SMR-DDI, a self-supervised framework that leverages contrastive learning to embed drugs into a scaffold-based feature space. Molecular scaffolds represent the core structural motifs that drive pharmacological activities, making them valuable for learning informative representations. Specifically, we pre-trained SMR-DDI on a large-scale unlabeled molecular dataset. We generated augmented views for each molecule via SMILES enumeration and optimized the embedding process through contrastive loss minimization between views. This enables the model to capture relevant and robust molecular features while reducing noise. We then transfer the learned representations for the downstream prediction of DDI. Experiments show that the new feature space has comparable expressivity to state-of-the-art molecular representations and achieved competitive DDI prediction results while training on less data. Additional investigations also revealed that pre-training on more extensive and diverse unlabeled molecular datasets improved the model's capability to embed molecules more effectively. Our results highlight contrastive learning as a promising approach for DDI prediction that can identify potentially hazardous drug combinations using only structural information.
Collapse
Affiliation(s)
- Rogia Kpanou
- Département d'informatique et Génie Logiciel, Université Laval, Québec City, QC, Canada.
| | - Patrick Dallaire
- Département d'informatique et Génie Logiciel, Université Laval, Québec City, QC, Canada
| | - Elsa Rousseau
- Département d'informatique et Génie Logiciel, Université Laval, Québec City, QC, Canada
- Centre de Recherche en Données Massives de l'Université Laval, Québec City, QC, Canada
- Centre Nutrition, Santé et Société (NUTRISS), Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec City, QC, Canada
| | - Jacques Corbeil
- Centre de Recherche en Données Massives de l'Université Laval, Québec City, QC, Canada.
- Centre de Recherche en Infectiologie de l'Université Laval, Axe Maladies Infectieuses et Immunitaires, Centre de Recherche du CHU de Québec-Université Laval, Québec City, QC, Canada.
- Département de Médecine Moléculaire, Faculté de Médecine, Université Laval, Québec City, QC, Canada.
| |
Collapse
|
22
|
Aguilera-Puga MDC, Cancelarich NL, Marani MM, de la Fuente-Nunez C, Plisson F. Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence. Methods Mol Biol 2024; 2714:329-352. [PMID: 37676607 DOI: 10.1007/978-1-0716-3441-7_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.
Collapse
Affiliation(s)
- Mariana D C Aguilera-Puga
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico
| | - Natalia L Cancelarich
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Mariela M Marani
- Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA.
| | - Fabien Plisson
- Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico.
- CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico.
| |
Collapse
|
23
|
Jiang H, Sun Z, Tian Y. ComCo: Complementary supervised contrastive learning for complementary label learning. Neural Netw 2024; 169:44-56. [PMID: 37857172 DOI: 10.1016/j.neunet.2023.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 09/04/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023]
Abstract
Complementary label learning (CLL) is an important problem that aims to reduce the cost of obtaining large-scale accurate datasets by only allowing each training sample to be equipped with labels the sample does not belong. Despite its promise, CLL remains a challenging task. Previous methods have proposed new loss functions or introduced deep learning-based models to CLL, but they mostly overlook the semantic information that may be implicit in the complementary labels. In this work, we propose a novel method, ComCo, which leverages a contrastive learning framework to assist CLL. Our method includes two key strategies: a positive selection strategy that identifies reliable positive samples and a negative selection strategy that skillfully integrates and leverages the information in the complementary labels to construct a negative set. These strategies bring ComCo closer to supervised contrastive learning. Empirically, ComCo significantly achieves better representation learning and outperforms the baseline models and the current state-of-the-art by up to 14.61% in CLL.
Collapse
Affiliation(s)
- Haoran Jiang
- School of Mathematical and Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China; Research Center on Fictitious Economy and Data Science, University of Chinese Academy of Sciences, Beijing, 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Zhihao Sun
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Yingjie Tian
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China; Research Center on Fictitious Economy and Data Science, University of Chinese Academy of Sciences, Beijing, 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, University of Chinese Academy of Sciences, Beijing, 100190, China; MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, Beijing, 100190, China.
| |
Collapse
|
24
|
Maedera S, Mizuno T, Kusuhara H. Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo. Comput Biol Med 2024; 168:107748. [PMID: 38016375 DOI: 10.1016/j.compbiomed.2023.107748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/25/2023] [Accepted: 11/20/2023] [Indexed: 11/30/2023]
Abstract
Toxicopathological images acquired during safety assessment elucidate an individual's biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, toxicopathological images are mainly encoded as pathological findings, evaluated by pathologists, which introduces challenges when used as input for modeling, specifically in terms of representation capability and comparability. In this study, we assessed the usefulness of latent representations extracted from toxicopathological images using Convolutional Neural Network (CNN) in estimating compound properties in vivo. Special emphasis was placed on examining the impact of learning pathological findings, the depth of frozen layers during learning, and the selection of the layer for latent representation. Our findings demonstrate that a machine learning model fed with the latent representation as input surpassed the performance of a model directly employing pathological findings as input, particularly in the classification of a compound's Mechanism of Action and in predicting late-phase findings from early-phase images in repeated-dose tests. While learning pathological findings did improve accuracy, the magnitude of improvement was relatively modest. Similarly, the effect of freezing layers during learning was also limited. Notably, the selection of the layer for latent representation had a substantial impact on the accurate estimation of compound properties in vivo.
Collapse
Affiliation(s)
- Shotaro Maedera
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan
| | - Tadahaya Mizuno
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan.
| | - Hiroyuki Kusuhara
- Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan
| |
Collapse
|
25
|
Mukashyaka P, Sheridan TB, Foroughi Pour A, Chuang JH. SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images. EBioMedicine 2024; 99:104908. [PMID: 38101298 PMCID: PMC10733087 DOI: 10.1016/j.ebiom.2023.104908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/27/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Deep learning has revolutionized digital pathology, allowing automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs are broken into smaller images called tiles, and a neural network encodes each tile. Many recent works use supervised attention-based models to aggregate tile-level features into a slide-level representation, which is then used for downstream analysis. Training supervised attention-based models is computationally intensive, architecture optimization of the attention module is non-trivial, and labeled data are not always available. Therefore, we developed an unsupervised and fast approach called SAMPLER to generate slide-level representations. METHODS Slide-level representations of SAMPLER are generated by encoding the cumulative distribution functions of multiscale tile-level features. To assess effectiveness of SAMPLER, slide-level representations of breast carcinoma (BRCA), non-small cell lung carcinoma (NSCLC), and renal cell carcinoma (RCC) WSIs of The Cancer Genome Atlas (TCGA) were used to train separate classifiers distinguishing tumor subtypes in FFPE and frozen WSIs. In addition, BRCA and NSCLC classifiers were externally validated on frozen WSIs. Moreover, SAMPLER's attention maps identify regions of interest, which were evaluated by a pathologist. To determine time efficiency of SAMPLER, we compared runtime of SAMPLER with two attention-based models. SAMPLER concepts were used to improve the design of a context-aware multi-head attention model (context-MHA). FINDINGS SAMPLER-based classifiers were comparable to state-of-the-art attention deep learning models to distinguish subtypes of BRCA (AUC = 0.911 ± 0.029), NSCLC (AUC = 0.940 ± 0.018), and RCC (AUC = 0.987 ± 0.006) on FFPE WSIs (internal test sets). However, training SAMLER-based classifiers was >100 times faster. SAMPLER models successfully distinguished tumor subtypes on both internal and external test sets of frozen WSIs. Histopathological review confirmed that SAMPLER-identified high attention tiles contained subtype-specific morphological features. The improved context-MHA distinguished subtypes of BRCA and RCC (BRCA-AUC = 0.921 ± 0.027, RCC-AUC = 0.988 ± 0.010) with increased accuracy on internal test FFPE WSIs. INTERPRETATION Our unsupervised statistical approach is fast and effective for analyzing WSIs, with greatly improved scalability over attention-based deep learning methods. The high accuracy of SAMPLER-based classifiers and interpretable attention maps suggest that SAMPLER successfully encodes the distinct morphologies within WSIs and will be applicable to general histology image analysis problems. FUNDING This study was supported by the National Cancer Institute (Grant No. R01CA230031 and P30CA034196).
Collapse
Affiliation(s)
- Patience Mukashyaka
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
| | - Todd B Sheridan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Department of Pathology, Hartford Hospital, Hartford, CT, USA
| | | | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA.
| |
Collapse
|
26
|
Gao X, Zheng G. SMILE: Siamese Multi-scale Interactive- representation LEarning for Hierarchical Diffeomorphic Deformable image registration. Comput Med Imaging Graph 2024; 111:102322. [PMID: 38157671 DOI: 10.1016/j.compmedimag.2023.102322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/23/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024]
Abstract
Deformable medical image registration plays an important role in many clinical applications. It aims to find a dense deformation field to establish point-wise correspondences between a pair of fixed and moving images. Recently, unsupervised deep learning-based registration methods have drawn more and more attention because of fast inference at testing stage. Despite remarkable progress, existing deep learning-based methods suffer from several limitations including: (a) they often overlook the explicit modeling of feature correspondences due to limited receptive fields; (b) the performance on image pairs with large spatial displacements is still limited since the dense deformation field is regressed from features learned by local convolutions; and (c) desirable properties, including topology-preservation and the invertibility of transformation, are often ignored. To address above limitations, we propose a novel Convolutional Neural Network (CNN) consisting of a Siamese Multi-scale Interactive-representation LEarning (SMILE) encoder and a Hierarchical Diffeomorphic Deformation (HDD) decoder. Specifically, the SMILE encoder aims for effective feature representation learning and spatial correspondence establishing while the HDD decoder seeks to regress the dense deformation field in a coarse-to-fine manner. We additionally propose a novel Local Invertible Loss (LIL) to encourage topology-preservation and local invertibility of the regressed transformation while keeping high registration accuracy. Extensive experiments conducted on two publicly available brain image datasets demonstrate the superiority of our method over the state-of-the-art (SOTA) approaches. Specifically, on the Neurite-OASIS dataset, our method achieved an average DSC of 0.815 and an average ASSD of 0.633 mm.
Collapse
Affiliation(s)
- Xiaoru Gao
- Institute of Medical Robotics, School of Biomedical Engineering, 800 DongChuan Road, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Guoyan Zheng
- Institute of Medical Robotics, School of Biomedical Engineering, 800 DongChuan Road, Shanghai Jiao Tong University, Shanghai, 200240, China.
| |
Collapse
|
27
|
Mancisidor RA, Kampffmeyer M, Aas K, Jenssen R. Discriminative multimodal learning via conditional priors in generative models. Neural Netw 2024; 169:417-430. [PMID: 37931473 DOI: 10.1016/j.neunet.2023.10.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/15/2023] [Accepted: 10/30/2023] [Indexed: 11/08/2023]
Abstract
Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data, which depict an object from different viewpoints. These two learning mechanisms can, however, conflict with each other and representations can fail to embed information on the data modalities. This research studies the realistic scenario in which all modalities and class labels are available for model training, e.g. images or handwriting, but where some modalities and labels required for downstream tasks are missing, e.g. text or annotations. We show, in this scenario, that the variational lower bound limits mutual information between joint representations and missing modalities. We, to counteract these problems, introduce a novel conditional multi-modal discriminative model that uses an informative prior distribution and optimizes a likelihood-free objective function that maximizes mutual information between joint representations and missing modalities. Extensive experimentation demonstrates the benefits of our proposed model, empirical results show that our model achieves state-of-the-art results in representative problems such as downstream classification, acoustic inversion, and image and annotation generation.
Collapse
Affiliation(s)
- Rogelio A Mancisidor
- Department of Data Science and Analytics, BI Norwegian Business School, Nydalsveien 37, 0484 Oslo, Norway.
| | - Michael Kampffmeyer
- Department of Physics and Technology, Faculty of Science and Technology, UiT The Arctic University of Norway, Hansine Hansens veg 18, 9037 Tromsø, Norway; Norwegian Computing Center, P.O. Box 114 Blindern Oslo, Norway.
| | - Kjersti Aas
- Norwegian Computing Center, P.O. Box 114 Blindern Oslo, Norway.
| | - Robert Jenssen
- Department of Physics and Technology, Faculty of Science and Technology, UiT The Arctic University of Norway, Hansine Hansens veg 18, 9037 Tromsø, Norway; Norwegian Computing Center, P.O. Box 114 Blindern Oslo, Norway.
| |
Collapse
|
28
|
Menghi N, Silvestrin F, Pascolini L, Penny W. The emergence of task-relevant representations in a nonlinear decision-making task. Neurobiol Learn Mem 2023; 206:107860. [PMID: 37952773 DOI: 10.1016/j.nlm.2023.107860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 09/26/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
This paper describes the relationship between performance in a decision-making task and the emergence of task-relevant representations. Participants learnt two tasks in which the appropriate response depended on multiple relevant stimuli and the underlying stimulus-outcome associations were governed by a latent feature that participants could discover. We divided participants into good and bad performers based on their overall classification rate and computed behavioural accuracy for each feature value. We found that participants with better performance had a better representation of the latent feature space. We then used representation similarity analysis on Electroencephalographic (EEG) data to identify when these representations emerge. We were able to decode task-relevant representations in a time window emerging 700 ms after stimulus presentation, but only for participants with good task performance. Our findings suggest that, in order to make good decisions, it is necessary to create and extract a low-dimensional representation of the task at hand.
Collapse
Affiliation(s)
- N Menghi
- University East Anglia, School of Psychology, UK; Max Planck for Human Cognitive and Brain Sciences, Department of Psychology, Germany.
| | - F Silvestrin
- University East Anglia, School of Psychology, UK
| | - L Pascolini
- University East Anglia, School of Psychology, UK
| | - W Penny
- University East Anglia, School of Psychology, UK
| |
Collapse
|
29
|
An Y, Cai G, Chen X, Guo L. PARSE: A personalized clinical time-series representation learning framework via abnormal offsets analysis. Comput Methods Programs Biomed 2023; 242:107838. [PMID: 37832431 DOI: 10.1016/j.cmpb.2023.107838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 09/18/2023] [Accepted: 10/01/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Clinical risk prediction of patients is an important research issue in the field of healthcare, which is of great significance for the diagnosis, treatment and prevention of diseases. In recent years, a large number of deep learning-based methods have been proposed for clinical prediction by mining relevant features of patients' health condition from historical Electronic Health Records (EHRs) data. However, most of these existing methods only focus on discovering the time series characteristics of physiological indexes such as laboratory tests and physical examinations, and fail to comprehensively consider the deviation degree of these physiological indexes from the normal range and their stability, thus greatly limiting the prediction performance. METHODS We propose a personalized clinical time-series representation learning framework via abnormal offsets analysis named PARSE for clinical risk prediction. In PARSE, while extracting relevant temporal features from the original EHR data, we further capture relevant features of abnormal condition as complementary information from the absolute offset of each physiological index's observed values from its normal value and the relative offset between each physiological index's observed values in two adjacent time steps. Finally, an adaptive fusion module is introduced to effectively integrate the above features to obtain the personalized patient's representations for clinical risk prediction. RESULTS We conduct an in-hospital mortality prediction task on two public real-world datasets. PARSE achieves the highest F1 scores of 48.1% and 40.3%, outperforming the state-of-the-art methods with a boost of 2.4% and 6.2% on two datasets respectively. Furthermore, the results of ablation experiments demonstrate that the two abnormal offsets and the proposed adaptive fusion method are contributing. CONCLUSIONS PARSE can better extract the risk-related information from the EHRs data and improve the personalization of the patients' representations. Each part of PARSE improves the final prediction performance independently.
Collapse
Affiliation(s)
- Ying An
- Big Data Institute, Central South University, Changsha, 410083, P.R. China.
| | - Guanglei Cai
- Big Data Institute, Central South University, Changsha, 410083, P.R. China; School of Computer Science and Engineering, Central South University, Changsha, 410083, P.R. China.
| | - Xianlai Chen
- Big Data Institute, Central South University, Changsha, 410083, P.R. China.
| | - Lin Guo
- Big Data Institute, Central South University, Changsha, 410083, P.R. China.
| |
Collapse
|
30
|
Zhao BW, Su XR, Yang Y, Li DX, Li GD, Hu PW, Zhao YG, Hu L. Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks. Methods 2023; 220:106-114. [PMID: 37972913 DOI: 10.1016/j.ymeth.2023.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/13/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023] Open
Abstract
Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.
Collapse
Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yue Yang
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Dong-Xu Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Guo-Dong Li
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Peng-Wei Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| | - Yong-Gang Zhao
- Department of Orthopaedic Surgery (hand and foot trauma), People's Hospital of Dongxihu, Wuhan 420100, China.
| | - Lun Hu
- The Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China.
| |
Collapse
|
31
|
Gu Y, Otake Y, Uemura K, Soufi M, Takao M, Talbot H, Okada S, Sugano N, Sato Y. Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography. Med Image Anal 2023; 90:102970. [PMID: 37774535 DOI: 10.1016/j.media.2023.102970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/25/2023] [Accepted: 09/11/2023] [Indexed: 10/01/2023]
Abstract
Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.
Collapse
Affiliation(s)
- Yi Gu
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan; CentraleSupélec, Université Paris-Saclay, Inria, Gif-sur-Yvette 91190, France.
| | - Yoshito Otake
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.
| | - Keisuke Uemura
- Department of Orthopeadic Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan.
| | - Mazen Soufi
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan
| | - Masaki Takao
- Department of Bone and Joint Surgery, Ehime University Graduate School of Medicine, Toon, Ehime 791-0295, Japan
| | - Hugues Talbot
- CentraleSupélec, Université Paris-Saclay, Inria, Gif-sur-Yvette 91190, France
| | - Seiji Okada
- Department of Orthopaedics, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Nobuhiko Sugano
- Department of Orthopeadic Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
| | - Yoshinobu Sato
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.
| |
Collapse
|
32
|
Mavaie P, Holder L, Skinner MK. Hybrid deep learning approach to improve classification of low-volume high-dimensional data. BMC Bioinformatics 2023; 24:419. [PMID: 37936066 PMCID: PMC10631218 DOI: 10.1186/s12859-023-05557-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 11/01/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND The performance of machine learning classification methods relies heavily on the choice of features. In many domains, feature generation can be labor-intensive and require domain knowledge, and feature selection methods do not scale well in high-dimensional datasets. Deep learning has shown success in feature generation but requires large datasets to achieve high classification accuracy. Biology domains typically exhibit these challenges with numerous handcrafted features (high-dimensional) and small amounts of training data (low volume). METHOD A hybrid learning approach is proposed that first trains a deep network on the training data, extracts features from the deep network, and then uses these features to re-express the data for input to a non-deep learning method, which is trained to perform the final classification. RESULTS The approach is systematically evaluated to determine the best layer of the deep learning network from which to extract features and the threshold on training data volume that prefers this approach. Results from several domains show that this hybrid approach outperforms standalone deep and non-deep learning methods, especially on low-volume, high-dimensional datasets. The diverse collection of datasets further supports the robustness of the approach across different domains. CONCLUSIONS The hybrid approach combines the strengths of deep and non-deep learning paradigms to achieve high performance on high-dimensional, low volume learning tasks that are typical in biology domains.
Collapse
Affiliation(s)
- Pegah Mavaie
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164, USA
| | - Lawrence Holder
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164, USA
| | - Michael K Skinner
- School of Biological Sciences, Center for Reproductive Biology, Washington State University, Pullman, WA, 99164-4236, USA.
| |
Collapse
|
33
|
Li J, Gao H, Qiang W, Zheng C. Information theory-guided heuristic progressive multi-view coding. Neural Netw 2023; 167:415-432. [PMID: 37673028 DOI: 10.1016/j.neunet.2023.08.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/17/2023] [Accepted: 08/17/2023] [Indexed: 09/08/2023]
Abstract
Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to different views in a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations; the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated; evenly measuring the similarities between terms might interfere with optimization. Importantly, few works study the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely Information theory-guided heuristic Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC constructs self-adjusted contrasting pools, which are adaptively modified by a view filter. Lastly, in the instance-tier, we adopt a designed unified loss to learn representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods.
Collapse
Affiliation(s)
- Jiangmeng Li
- Science & Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Hang Gao
- Science & Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Wenwen Qiang
- Science & Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Changwen Zheng
- Science & Technology on Integrated Information System Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
34
|
Dumais F, Legarreta JH, Lemaire C, Poulin P, Rheault F, Petit L, Barakovic M, Magon S, Descoteaux M, Jodoin PM. FIESTA: Autoencoders for accurate fiber segmentation in tractography. Neuroimage 2023; 279:120288. [PMID: 37495198 DOI: 10.1016/j.neuroimage.2023.120288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 07/20/2023] [Indexed: 07/28/2023] Open
Abstract
White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework.
Collapse
Affiliation(s)
- Félix Dumais
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada.
| | - Jon Haitz Legarreta
- Department of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, USA
| | - Carl Lemaire
- Centre de Calcul Scientifique, Université de Sherbrooke, Canada
| | - Philippe Poulin
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada
| | - François Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, Université de Sherbrooke, Canada
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle (GIN), CNRS, CEA, IMN, GIN, UMR 5293, F-33000 Bordeaux, Université de Bordeaux, France
| | - Muhamed Barakovic
- Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Stefano Magon
- Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Imeka Solutions inc, Sherbrooke, Canada
| | - Pierre-Marc Jodoin
- Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada; Imeka Solutions inc, Sherbrooke, Canada
| |
Collapse
|
35
|
Tian Y, Bai K, Yu X, Zhu S. Causal multi-label learning for image classification. Neural Netw 2023; 167:626-637. [PMID: 37716214 DOI: 10.1016/j.neunet.2023.08.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/24/2023] [Accepted: 08/29/2023] [Indexed: 09/18/2023]
Abstract
In this paper, we investigate the problem of causal image classification with multi-label learning. As multi-label learning involves a diversity of supervision signals, it is considered a challenging issue to solve. Previous approaches have attempted to improve performance by identifying label-related image areas or exploiting the co-occurrence of labels. However, these methods are often characterized by complicated procedures, tedious computations, and a lack of intuitive interpretations. To overcome these limitations, we propose a novel approach that incorporates the concept of causal inference, which has been shown to be beneficial in other computer vision problems. Our method, called causal multi-label learning (CMLL), enables the selection of multiple objects from the original image through a multi-class attention module. These objects are then subjected to causal intervention to learn the causal relationships between different labels. Our proposed approach is both elegant and effective, with low computational cost and few parameters required for the multi-class causal intervention approach. Extensive tests and ablation studies demonstrate that the proposed method significantly improves prediction performance without a significant increase in training and inference times.
Collapse
Affiliation(s)
- Yingjie Tian
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China; MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS, Beijing 100190, China.
| | - Kunlong Bai
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China.
| | - Xiaotong Yu
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China.
| | - Siyu Zhu
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China.
| |
Collapse
|
36
|
He C, Qu Y, Yin J, Zhao Z, Ma R, Duan L. Cross-view contrastive representation learning approach to predicting DTIs via integrating multi-source information. Methods 2023; 218:176-188. [PMID: 37586602 DOI: 10.1016/j.ymeth.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/26/2023] [Accepted: 08/08/2023] [Indexed: 08/18/2023] Open
Abstract
Drug-target interaction (DTI) prediction serves as the foundation of new drug findings and drug repositioning. For drugs/targets, the sequence data contains the biological structural information, while the heterogeneous network contains the biochemical functional information. These two types of information describe different aspects of drugs and targets. Due to the complexity of DTI machinery, it is necessary to learn the representation from multiple perspectives. We hereby try to design a way to leverage information from multi-source data to the maximum extent and find a strategy to fuse them. To address the above challenges, we propose a model, named MOVE (short for integrating multi-source information for predicting DTI via cross-view contrastive learning), for learning comprehensive representations of each drug and target from multi-source data. MOVE extracts information from the sequence view and the network view, then utilizes a fusion module with auxiliary contrastive learning to facilitate the fusion of representations. Experimental results on the benchmark dataset demonstrate that MOVE is effective in DTI prediction.
Collapse
Affiliation(s)
- Chengxin He
- School of Computer Science, Sichuan University, Chengdu 610065, China; Med-X Center for Informatics, Sichuan University, Chengdu 610065, China
| | - Yuening Qu
- School of Computer Science, Sichuan University, Chengdu 610065, China
| | - Jin Yin
- The West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610065, China
| | - Zhenjiang Zhao
- School of Computer Science, Sichuan University, Chengdu 610065, China
| | - Runze Ma
- School of Computer Science, Sichuan University, Chengdu 610065, China
| | - Lei Duan
- School of Computer Science, Sichuan University, Chengdu 610065, China; Med-X Center for Informatics, Sichuan University, Chengdu 610065, China.
| |
Collapse
|
37
|
Xu M, Zhu Z, Li Y, Zheng S, Li L, Wu H, Zhao Y. Cooperative dual medical ontology representation learning for clinical assisted decision-making. Comput Biol Med 2023; 163:107138. [PMID: 37329613 DOI: 10.1016/j.compbiomed.2023.107138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/28/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
OBJECTIVE Predicting clinical events and providing assisted decision-making using Electronic Health Records (EHRs) play a central role in personalized healthcare. Despite the promising performance achieved for diagnosis and procedure predictions, most of the existing predictive models regard different medical codes as the same type and generally ignore the dependence between diagnoses and procedures in patients' admission history. To address these issues, we propose an end-to-end cooperative dual medical ontology representation learning framework for clinical assisted decision-making. MATERIALS AND METHODS The framework consists of two primary modules: (1) dual medical ontology representation learning to facilitate the learning of medical concepts and (2) task dependent multi-task prediction to capture the correlation between diagnoses and procedures in patients' admission history. We evaluate our method with EHRs from the MIMIC-III Clinical Database, covering 6321 patients and 16335 visits. RESULTS Experiments conducted on the MIMIC-III dataset show that the proposed model achieves the best performance, with a top-20 accuracy of 58.20% for diagnosis prediction and a top-20 accuracy of 75.85% for procedure prediction. In addition, a series of experimental analyses and case studies further illustrate the excellent performance of our model. CONCLUSION We propose an end-to-end cooperative dual medical ontology representation learning framework, which achieves superior performance on multi-task diagnosis and procedure predictions. The source code is available at https://github.com/mhxu1998/CoDMO.
Collapse
Affiliation(s)
- Muhao Xu
- Institute of Information Science, Beijing Jiaotong University, Beijing, China; Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China
| | - Zhenfeng Zhu
- Institute of Information Science, Beijing Jiaotong University, Beijing, China; Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China.
| | - Youru Li
- Institute of Information Science, Beijing Jiaotong University, Beijing, China; Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China
| | - Shuai Zheng
- Institute of Information Science, Beijing Jiaotong University, Beijing, China; Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China
| | - Linfeng Li
- Yidu Cloud Technology Inc., Beijing, China
| | - Haiyan Wu
- Department of Otorhinolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yao Zhao
- Institute of Information Science, Beijing Jiaotong University, Beijing, China; Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, China
| |
Collapse
|
38
|
Che L, Jin Y, Shi Y, Yu X, Sun H, Liu H, Li X. A drug molecular classification model based on graph structure generation. J Biomed Inform 2023; 145:104447. [PMID: 37481052 DOI: 10.1016/j.jbi.2023.104447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 07/14/2023] [Accepted: 07/16/2023] [Indexed: 07/24/2023]
Abstract
Molecular property prediction based on artificial intelligence technology has significant prospects in speeding up drug discovery and reducing drug discovery costs. Among them, molecular property prediction based on graph neural networks (GNNs) has received extensive attention in recent years. However, the existing graph neural networks still face the following challenges in node representation learning. First, the number of nodes increases exponentially with the expansion of the perception field, which limits the exploration ability of the model in the depth direction. Secondly, the large number of nodes in the perception field brings noise, which is not conducive to the model's representation learning of the key structures. Therefore, a graph neural network model based on structure generation is proposed in this paper. The model adopts the depth-first strategy to generate the key structures of the graph, to solve the problem of insufficient exploration ability of the graph neural network in the depth direction. A tendentious node selection method is designed to gradually select nodes and edges to generate the key structures of the graph, to solve the noise problem caused by the excessive number of nodes. In addition, the model skillfully realizes forward propagation and iterative optimization of structure generation by using an attention mechanism and random bias. Experimental results on public data sets show that the proposed model achieves better classification results than the existing best models.
Collapse
Affiliation(s)
- Lixuan Che
- College of Culture and Creativity, Weifang Vocational College, Weifang, China.
| | - Yide Jin
- Department of Statistics, University of Minnesota, Minneapolis, MN, USA.
| | - Yuliang Shi
- School of Software, Shandong University, Jinan, China; Dareway Software Co., Ltd, Jinan, China.
| | - Xiaojing Yu
- Department of Dermatology, Qilu Hospital, Shandong University, Jinan, China.
| | - Hongfeng Sun
- School of Data and Computer Science, Shandong Women's University, Jinan, China.
| | - Hui Liu
- School of Data and Computer Science, Shandong Women's University, Jinan, China.
| | - Xinyu Li
- Department of Dermatology, Qilu Hospital, Shandong University, Jinan, China.
| |
Collapse
|
39
|
Huang F, Deng Y. TCGAN: Convolutional Generative Adversarial Network for time series classification and clustering. Neural Netw 2023; 165:868-883. [PMID: 37433231 DOI: 10.1016/j.neunet.2023.06.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 06/04/2023] [Accepted: 06/25/2023] [Indexed: 07/13/2023]
Abstract
Recent works have demonstrated the superiority of supervised Convolutional Neural Networks (CNNs) in learning hierarchical representations from time series data for successful classification. These methods require sufficiently large labeled data for stable learning, however acquiring high-quality labeled time series data can be costly and potentially infeasible. Generative Adversarial Networks (GANs) have achieved great success in enhancing unsupervised and semi-supervised learning. Nonetheless, to our best knowledge, it remains unclear how effectively GANs can serve as a general-purpose solution to learn representations for time series recognition, i.e., classification and clustering. The above considerations inspire us to introduce a Time-series Convolutional GAN (TCGAN). TCGAN learns by playing an adversarial game between two one-dimensional CNNs (i.e., a generator and a discriminator) in the absence of label information. Parts of the trained TCGAN are then reused to construct a representation encoder to empower linear recognition methods. We conducted comprehensive experiments on synthetic and real-world datasets. The results demonstrate that TCGAN is faster and more accurate than existing time-series GANs. The learned representations enable simple classification and clustering methods to achieve superior and stable performance. Furthermore, TCGAN retains high efficacy in scenarios with few-labeled and imbalanced-labeled data. Our work provides a promising path to effectively utilize abundant unlabeled time series data.
Collapse
Affiliation(s)
- Fanling Huang
- School of Software, Tsinghua University, Beijing, China.
| | - Yangdong Deng
- School of Software, Tsinghua University, Beijing, China.
| |
Collapse
|
40
|
Liu S, Ozay M. Task guided representation learning using compositional models for zero-shot domain adaptation. Neural Netw 2023; 165:370-380. [PMID: 37329781 DOI: 10.1016/j.neunet.2023.05.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 04/29/2023] [Accepted: 05/17/2023] [Indexed: 06/19/2023]
Abstract
Zero-shot domain adaptation (ZDA) methods aim to transfer knowledge about a task learned in a source domain to a target domain, while task-relevant data from target domain are not available. In this work, we address learning feature representations which are invariant to and shared among different domains considering task characteristics for ZDA. To this end, we propose a method for task-guided ZDA (TG-ZDA) which employs multi-branch deep neural networks to learn feature representations exploiting their domain invariance and shareability properties. The proposed TG-ZDA models can be trained end-to-end without requiring synthetic tasks and data generated from estimated representations of target domains. The proposed TG-ZDA has been examined using benchmark ZDA tasks on image classification datasets. Experimental results show that our proposed TG-ZDA outperforms state-of-the-art ZDA methods for different domains and tasks.
Collapse
Affiliation(s)
- Shuang Liu
- RIKEN Center for AIP, Nihonbashi 1-chome Mitsui Building, Tokyo, 1030027, Japan.
| | - Mete Ozay
- Middle East Technical University, Dumlupınar Bulvarı No:1, Ankara, 06800, Turkey.
| |
Collapse
|
41
|
DiPalma J, Torresani L, Hassanpour S. HistoPerm: A permutation-based view generation approach for improving histopathologic feature representation learning. J Pathol Inform 2023; 14:100320. [PMID: 37457594 PMCID: PMC10339175 DOI: 10.1016/j.jpi.2023.100320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/23/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023] Open
Abstract
Deep learning has been effective for histology image analysis in digital pathology. However, many current deep learning approaches require large, strongly- or weakly labeled images and regions of interest, which can be time-consuming and resource-intensive to obtain. To address this challenge, we present HistoPerm, a view generation method for representation learning using joint embedding architectures that enhances representation learning for histology images. HistoPerm permutes augmented views of patches extracted from whole-slide histology images to improve classification performance. We evaluated the effectiveness of HistoPerm on 2 histology image datasets for Celiac disease and Renal Cell Carcinoma, using 3 widely used joint embedding architecture-based representation learning methods: BYOL, SimCLR, and VICReg. Our results show that HistoPerm consistently improves patch- and slide-level classification performance in terms of accuracy, F1-score, and AUC. Specifically, for patch-level classification accuracy on the Celiac disease dataset, HistoPerm boosts BYOL and VICReg by 8% and SimCLR by 3%. On the Renal Cell Carcinoma dataset, patch-level classification accuracy is increased by 2% for BYOL and VICReg, and by 1% for SimCLR. In addition, on the Celiac disease dataset, models with HistoPerm outperform the fully supervised baseline model by 6%, 5%, and 2% for BYOL, SimCLR, and VICReg, respectively. For the Renal Cell Carcinoma dataset, HistoPerm lowers the classification accuracy gap for the models up to 10% relative to the fully supervised baseline. These findings suggest that HistoPerm can be a valuable tool for improving representation learning of histopathology features when access to labeled data is limited and can lead to whole-slide classification results that are comparable to or superior to fully supervised methods.
Collapse
Affiliation(s)
- Joseph DiPalma
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
| | - Lorenzo Torresani
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
| | - Saeed Hassanpour
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
| |
Collapse
|
42
|
Czolbe S, Pegios P, Krause O, Feragen A. Semantic similarity metrics for image registration. Med Image Anal 2023; 87:102830. [PMID: 37172390 DOI: 10.1016/j.media.2023.102830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 01/19/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023]
Abstract
Image registration aims to find geometric transformations that align images. Most algorithmic and deep learning-based methods solve the registration problem by minimizing a loss function, consisting of a similarity metric comparing the aligned images, and a regularization term ensuring smoothness of the transformation. Existing similarity metrics like Euclidean Distance or Normalized Cross-Correlation focus on aligning pixel intensity values or correlations, giving difficulties with low intensity contrast, noise, and ambiguous matching. We propose a semantic similarity metric for image registration, focusing on aligning image areas based on semantic correspondence instead. Our approach learns dataset-specific features that drive the optimization of a learning-based registration model. We train both an unsupervised approach extracting features with an auto-encoder, and a semi-supervised approach using supplemental segmentation data. We validate the semantic similarity metric using both deep-learning-based and algorithmic image registration methods. Compared to existing methods across four different image modalities and applications, the method achieves consistently high registration accuracy and smooth transformation fields.
Collapse
Affiliation(s)
- Steffen Czolbe
- Department of Computer Science, University of Copenhagen, Denmark.
| | | | - Oswin Krause
- Department of Computer Science, University of Copenhagen, Denmark
| | - Aasa Feragen
- DTU Compute, Technical University of Denmark, Denmark
| |
Collapse
|
43
|
Daniali M, Galer PD, Lewis-Smith D, Parthasarathy S, Kim E, Salvucci DD, Miller JM, Haag S, Helbig I. Enriching representation learning using 53 million patient notes through human phenotype ontology embedding. Artif Intell Med 2023; 139:102523. [PMID: 37100502 PMCID: PMC10782859 DOI: 10.1016/j.artmed.2023.102523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023]
Abstract
The Human Phenotype Ontology (HPO) is a dictionary of >15,000 clinical phenotypic terms with defined semantic relationships, developed to standardize phenotypic analysis. Over the last decade, the HPO has been used to accelerate the implementation of precision medicine into clinical practice. In addition, recent research in representation learning, specifically in graph embedding, has led to notable progress in automated prediction via learned features. Here, we present a novel approach to phenotype representation by incorporating phenotypic frequencies based on 53 million full-text health care notes from >1.5 million individuals. We demonstrate the efficacy of our proposed phenotype embedding technique by comparing our work to existing phenotypic similarity-measuring methods. Using phenotype frequencies in our embedding technique, we are able to identify phenotypic similarities that surpass current computational models. Furthermore, our embedding technique exhibits a high degree of agreement with domain experts' judgment. By transforming complex and multidimensional phenotypes from the HPO format into vectors, our proposed method enables efficient representation of these phenotypes for downstream tasks that require deep phenotyping. This is demonstrated in a patient similarity analysis and can further be applied to disease trajectory and risk prediction.
Collapse
Affiliation(s)
- Maryam Daniali
- Department of Computer Science, Drexel University, Philadelphia, PA, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Peter D Galer
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy Neuro Genetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - David Lewis-Smith
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy Neuro Genetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK; Department of Clinical Neurosciences, Royal Victoria Infirmary, Newcastle-upon-Tyne, UK
| | - Shridhar Parthasarathy
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy Neuro Genetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Edward Kim
- Department of Computer Science, Drexel University, Philadelphia, PA, USA
| | - Dario D Salvucci
- Department of Computer Science, Drexel University, Philadelphia, PA, USA
| | - Jeffrey M Miller
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Scott Haag
- Department of Computer Science, Drexel University, Philadelphia, PA, USA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ingo Helbig
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy Neuro Genetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
| |
Collapse
|
44
|
van Tulder G, de Bruijne M. Unpaired, unsupervised domain adaptation assumes your domains are already similar. Med Image Anal 2023; 87:102825. [PMID: 37116296 DOI: 10.1016/j.media.2023.102825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 03/30/2023] [Accepted: 04/17/2023] [Indexed: 04/30/2023]
Abstract
Unsupervised domain adaptation is a popular method in medical image analysis, but it can be tricky to make it work: without labels to link the domains, domains must be matched using feature distributions. If there is no additional information, this often leaves a choice between multiple possibilities to map the data that may be equally likely but not equally correct. In this paper we explore the fundamental problems that may arise in unsupervised domain adaptation, and discuss conditions that might still make it work. Focusing on medical image analysis, we argue that images from different domains may have similar class balance, similar intensities, similar spatial structure, or similar textures. We demonstrate how these implicit conditions can affect domain adaptation performance in experiments with synthetic data, MNIST digits, and medical images. We observe that practical success of unsupervised domain adaptation relies on existing similarities in the data, and is anything but guaranteed in the general case. Understanding these implicit assumptions is a key step in identifying potential problems in domain adaptation and improving the reliability of the results.
Collapse
Affiliation(s)
- Gijs van Tulder
- Data Science group, Faculty of Science, Radboud University, Postbus 9010, 6500 GL Nijmegen, The Netherlands; Biomedical Imaging Group, Erasmus MC, Postbus 2040, 3000 CA Rotterdam, The Netherlands.
| | - Marleen de Bruijne
- Biomedical Imaging Group, Erasmus MC, Postbus 2040, 3000 CA Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark.
| |
Collapse
|
45
|
Ullah Z, Usman M, Gwak J. MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images. Expert Syst Appl 2023; 216:119475. [PMID: 36619348 PMCID: PMC9810379 DOI: 10.1016/j.eswa.2022.119475] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 07/28/2022] [Accepted: 12/22/2022] [Indexed: 06/12/2023]
Abstract
Efficient diagnosis of COVID-19 plays an important role in preventing the spread of the disease. There are three major modalities to diagnose COVID-19 which include polymerase chain reaction tests, computed tomography scans, and chest X-rays (CXRs). Among these, diagnosis using CXRs is the most economical approach; however, it requires extensive human expertise to diagnose COVID-19 in CXRs, which may deprive it of cost-effectiveness. The computer-aided diagnosis with deep learning has the potential to perform accurate detection of COVID-19 in CXRs without human intervention while preserving its cost-effectiveness. Many efforts have been made to develop a highly accurate and robust solution. However, due to the limited amount of labeled data, existing solutions are evaluated on a small set of test dataset. In this work, we proposed a solution to this problem by using a multi-task semi-supervised learning (MTSSL) framework that utilized auxiliary tasks for which adequate data is publicly available. Specifically, we utilized Pneumonia, Lung Opacity, and Pleural Effusion as additional tasks using the ChesXpert dataset. We illustrated that the primary task of COVID-19 detection, for which only limited labeled data is available, can be improved by using this additional data. We further employed an adversarial autoencoder (AAE), which has a strong capability to learn powerful and discriminative features, within our MTSSL framework to maximize the benefit of multi-task learning. In addition, the supervised classification networks in combination with the unsupervised AAE empower semi-supervised learning, which includes a discriminative part in the unsupervised AAE training pipeline. The generalization of our framework is improved due to this semi-supervised learning and thus it leads to enhancement in COVID-19 detection performance. The proposed model is rigorously evaluated on the largest publicly available COVID-19 dataset and experimental results show that the proposed model attained state-of-the-art performance.
Collapse
Affiliation(s)
- Zahid Ullah
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
| | - Muhammad Usman
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, South Korea
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, South Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, South Korea
- Department of IT Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, South Korea
| |
Collapse
|
46
|
Jing C, Huang Y, Zhuang Y, Sun L, Xiao Z, Huang Y, Ding X. Exploring personalization via federated representation Learning on non-IID data. Neural Netw 2023; 163:354-366. [PMID: 37099898 DOI: 10.1016/j.neunet.2023.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 02/13/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023]
Abstract
Federated Learning (FL) can learn a global model across decentralized data over different clients. However, it is susceptible to statistical heterogeneity of client-specific data. Clients focus on optimizing for their individual target distributions, which would yield divergence of the global model due to inconsistent data distributions. Moreover, federated learning approaches adhere to the scheme of collaboratively learning representations and classifiers, further exacerbating such inconsistency and resulting in imbalanced features and biased classifiers. Hence, in this paper, we propose an independent two-stage personalized FL framework, i.e., Fed-RepPer, to separate representation learning from classification in federated learning. First, the client-side feature representation models are learned using supervised contrastive loss, which enables local objectives consistently, i.e., learning robust representations on distinct data distributions. Local representation models are aggregated into the common global representation model. Then, in the second stage, personalization is studied by learning different classifiers for each client based on the global representation model. The proposed two-stage learning scheme is examined in lightweight edge computing that involves devices with constrained computation resources. Experiments on various datasets (CIFAR-10/100, CINIC-10) and heterogeneous data setups show that Fed-RepPer outperforms alternatives by utilizing flexibility and personalization on non-IID data.
Collapse
Affiliation(s)
- Changxing Jing
- School of Informatics, Xiamen University, Xiamen, 361005, Fujian, China
| | - Yan Huang
- College of Computing and Software Engineering, Kennesaw State University, Kennesaw, 30144, GA, USA
| | - Yihong Zhuang
- School of Informatics, Xiamen University, Xiamen, 361005, Fujian, China
| | - Liyan Sun
- Department of Radiation Oncology, Stanford University, Stanford, CA 94035, USA
| | - Zhenlong Xiao
- School of Informatics, Xiamen University, Xiamen, 361005, Fujian, China
| | - Yue Huang
- School of Informatics, Xiamen University, Xiamen, 361005, Fujian, China
| | - Xinghao Ding
- School of Informatics, Xiamen University, Xiamen, 361005, Fujian, China; Institute of Artificial Intelligence, Xiamen University, Xiamen, 361005, Fujian, China.
| |
Collapse
|
47
|
Legarreta JH, Petit L, Jodoin PM, Descoteaux M. Generative Sampling in Bundle Tractography using Autoencoders (GESTA). Med Image Anal 2023; 85:102761. [PMID: 36773366 DOI: 10.1016/j.media.2023.102761] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 01/11/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023]
Abstract
Current tractography methods use the local orientation information to propagate streamlines from seed locations. Many such seeds provide streamlines that stop prematurely or fail to map the true white matter pathways because some bundles are "harder-to-track" than others. This results in tractography reconstructions with poor white and gray matter spatial coverage. In this work, we propose a generative, autoencoder-based method, named GESTA (Generative Sampling in Bundle Tractography using Autoencoders), that produces streamlines achieving better spatial coverage. Compared to other deep learning methods, our autoencoder-based framework uses a single model to generate streamlines in a bundle-wise fashion, and does not require to propagate local orientations. GESTA produces new and complete streamlines for any given white matter bundle, including hard-to-track bundles. Applied on top of a given tractogram, GESTA is shown to be effective in improving the white matter volume coverage in poorly populated bundles, both on synthetic and human brain in vivo data. Our streamline evaluation framework ensures that the streamlines produced by GESTA are anatomically plausible and fit well to the local diffusion signal. The streamline evaluation criteria assess anatomy (white matter coverage), local orientation alignment (direction), and geometry features of streamlines, and optionally, gray matter connectivity. The GESTA framework offers considerable gains in bundle overlap using a reduced set of seeding streamlines with a 1.5x improvement for the "Fiber Cup", and 6x for the ISMRM 2015 Tractography Challenge datasets. Similarly, it provides a 4x white matter volume increase on the BIL&GIN callosal homotopic dataset, and successfully populates bundles on the multi-subject, multi-site, whole-brain in vivo TractoInferno dataset. GESTA is thus a novel deep generative bundle tractography method that can be used to improve the tractography reconstruction of the white matter.
Collapse
|
48
|
Tesei G, Giampanis S, Shi J, Norgeot B. Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimation. J Biomed Inform 2023; 140:104339. [PMID: 36940895 DOI: 10.1016/j.jbi.2023.104339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/15/2023] [Accepted: 03/13/2023] [Indexed: 03/23/2023]
Abstract
A causal effect can be defined as a comparison of outcomes that result from two or more alternative actions, with only one of the action-outcome pairs actually being observed. In healthcare, the gold standard for causal effect measurements is randomized controlled trials (RCTs), in which a target population is explicitly defined and each study sample is randomly assigned to either the treatment or control cohorts. The great potential to derive actionable insights from causal relationships has led to a growing body of machine-learning research applying causal effect estimators to observational data in the fields of healthcare, education, and economics. The primary difference between causal effect studies utilizing observational data and RCTs is that for observational data, the study occurs after the treatment, and therefore we do not have control over the treatment assignment mechanism. This can lead to massive differences in covariate distributions between control and treatment samples, making a comparison of causal effects confounded and unreliable. Classical approaches have sought to solve this problem piecemeal, first by predicting treatment assignment and then treatment effect separately. Recent work extended part of these approaches to a new family of representation-learning algorithms, showing that the upper bound of the expected treatment effect estimation error is determined by two factors: the outcome generalization-error of the representation and the distance between the treated and control distributions induced by the representation. To achieve minimal dissimilarity in learning such distributions, in this work we propose a specific auto-balancing, self-supervised objective. Experiments on real and benchmark datasets revealed that our approach consistently produced less biased estimates than previously published state-of-the-art methods. We demonstrate that the reduction in error can be directly attributed to the ability to learn representations that explicitly reduce such dissimilarity; further, in case of violations of the positivity assumption (frequent in observational data), we show our approach performs significantly better than the previous state of the art. Thus, by learning representations that induce similar distributions of the treated and control cohorts, we present evidence to support the error bound dissimilarity hypothesis as well as providing a new state-of-the-art model for causal effect estimation.
Collapse
Affiliation(s)
- Gino Tesei
- Elevance Health, Palo Alto, CA 94301, USA.
| | | | - Jingpu Shi
- Elevance Health, Palo Alto, CA 94301, USA.
| | | |
Collapse
|
49
|
Gajendran S, Manjula D, Sugumaran V, Hema R. Extraction of knowledge graph of Covid-19 through mining of unstructured biomedical corpora. Comput Biol Chem 2023; 102:107808. [PMID: 36621289 PMCID: PMC9807269 DOI: 10.1016/j.compbiolchem.2022.107808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/21/2022] [Accepted: 12/29/2022] [Indexed: 01/04/2023]
Abstract
The number of biomedical articles published is increasing rapidly over the years. Currently there are about 30 million articles in PubMed and over 25 million mentions in Medline. Among these fundamentals, Biomedical Named Entity Recognition (BioNER) and Biomedical Relation Extraction (BioRE) are the most essential in analysing the literature. In the biomedical domain, Knowledge Graph is used to visualize the relationships between various entities such as proteins, chemicals and diseases. Scientific publications have increased dramatically as a result of the search for treatments and potential cures for the new Coronavirus, but efficiently analysing, integrating, and utilising related sources of information remains a difficulty. In order to effectively combat the disease during pandemics like COVID-19, literature must be used quickly and effectively. In this paper, we introduced a fully automated framework consists of BERT-BiLSTM, Knowledge graph, and Representation Learning model to extract the top diseases, chemicals, and proteins related to COVID-19 from the literature. The proposed framework uses Named Entity Recognition models for disease recognition, chemical recognition, and protein recognition. Then the system uses the Chemical - Disease Relation Extraction and Chemical - Protein Relation Extraction models. And the system extracts the entities and relations from the CORD-19 dataset using the models. The system then creates a Knowledge Graph for the extracted relations and entities. The system performs Representation Learning on this KG to get the embeddings of all entities and get the top related diseases, chemicals, and proteins with respect to COVID-19.
Collapse
Affiliation(s)
- Sudhakaran Gajendran
- School of Electronics Engineering, Vellore Institute of Technology, Chennai, India,Corresponding author
| | - D. Manjula
- School of Computer Science Engineering, Vellore Institute of Technology, Chennai, India
| | - Vijayan Sugumaran
- Center for Data Science and Big Data Analytics, Oakland University, Rochester, MI, USA,Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, MI, USA
| | - R. Hema
- Department of Electronics and Communication Engineering, St. Joseph College of Engineering, Chennai, India
| |
Collapse
|
50
|
Lu M, Zhang Y, Zhang S, Shi H, Huang Z. Knowledge-aware patient representation learning for multiple disease subtypes. J Biomed Inform 2023; 138:104292. [PMID: 36641030 DOI: 10.1016/j.jbi.2023.104292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 01/10/2023] [Indexed: 01/13/2023]
Abstract
Learning latent representations of patients with a target disease is a core problem in a broad range of downstream applications, such as clinical endpoint prediction. The suffering of patients may have multiple subtypes with certain similarities and differences, which need to be addressed for learning effective patient representation to facilitate the downstream tasks. However, existing studies either ignore the distinction of disease subtypes to learn disease-level representations, or neglect the correlations between subtypes and only learn disease subtype-level representations, which affects the performance of patient representation learning. To alleviate this problem, we studied how to effectively integrate data from all disease subtypes to improve the representation of each subtype. Specifically, we proposed a knowledge-aware shared-private neural network model to explicitly use disease-oriented knowledge and learn shared and specific representations from the disease and its subtype perspectives. To evaluate the feasibility of the proposed model, we conducted a particular downstream task, i.e., clinical endpoint prediction, on the basis of the learned patient presentations. The results on the real-world clinical datasets demonstrated that our model could yield a significant improvement over state-of-the-art models.
Collapse
Affiliation(s)
- Menglin Lu
- College of Computer Science and Technology, Zhejiang University, 866 Yuhangtang Road, 310058 Hangzhou, People's Republic of China.
| | - Yujie Zhang
- College of Computer Science and Technology, Zhejiang University, 866 Yuhangtang Road, 310058 Hangzhou, People's Republic of China.
| | - Suixia Zhang
- College of Computer Science and Technology, Zhejiang University, 866 Yuhangtang Road, 310058 Hangzhou, People's Republic of China.
| | - Hanrui Shi
- College of Computer Science and Technology, Zhejiang University, 866 Yuhangtang Road, 310058 Hangzhou, People's Republic of China.
| | - Zhengxing Huang
- College of Computer Science and Technology, Zhejiang University, 866 Yuhangtang Road, 310058 Hangzhou, People's Republic of China.
| |
Collapse
|