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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.
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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.
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2
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Hansen S, Gautam S, Salahuddin SA, Kampffmeyer M, Jenssen R. ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement. Med Image Anal 2023; 89:102870. [PMID: 37541101 DOI: 10.1016/j.media.2023.102870] [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: 10/30/2022] [Revised: 04/23/2023] [Accepted: 06/12/2023] [Indexed: 08/06/2023]
Abstract
A major barrier to applying deep segmentation models in the medical domain is their typical data-hungry nature, requiring experts to collect and label large amounts of data for training. As a reaction, prototypical few-shot segmentation (FSS) models have recently gained traction as data-efficient alternatives. Nevertheless, despite the recent progress of these models, they still have some essential shortcomings that must be addressed. In this work, we focus on three of these shortcomings: (i) the lack of uncertainty estimation, (ii) the lack of a guiding mechanism to help locate edges and encourage spatial consistency in the segmentation maps, and (iii) the models' inability to do one-step multi-class segmentation. Without modifying or requiring a specific backbone architecture, we propose a modified prototype extraction module that facilitates the computation of uncertainty maps in prototypical FSS models, and show that the resulting maps are useful indicators of the model uncertainty. To improve the segmentation around boundaries and to encourage spatial consistency, we propose a novel feature refinement module that leverages structural information in the input space to help guide the segmentation in the feature space. Furthermore, we demonstrate how uncertainty maps can be used to automatically guide this feature refinement. Finally, to avoid ambiguous voxel predictions that occur when images are segmented class-by-class, we propose a procedure to perform one-step multi-class FSS. The efficiency of our proposed methodology is evaluated on two representative datasets for abdominal organ segmentation (CHAOS dataset and BTCV dataset) and one dataset for cardiac segmentation (MS-CMRSeg dataset). The results show that our proposed methodology significantly (one-sided Wilcoxon signed rank test, p<0.05) improves the baseline, increasing the overall dice score with +5.2, +5.1, and +2.8 percentage points for the CHAOS dataset, the BTCV dataset, and the MS-CMRSeg dataset, respectively.
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Affiliation(s)
- Stine Hansen
- Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway.
| | - Srishti Gautam
- Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway
| | - Suaiba Amina Salahuddin
- Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway
| | - Michael Kampffmeyer
- Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway
| | - Robert Jenssen
- Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway
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Wickstrøm KK, Løkse S, Kampffmeyer MC, Yu S, Príncipe JC, Jenssen R. Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy. Entropy (Basel) 2023; 25:899. [PMID: 37372243 DOI: 10.3390/e25060899] [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] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023]
Abstract
Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently to gain insight into, among others, DNNs' generalization ability. However, it is by no means obvious how to estimate the mutual information (MI) between each hidden layer and the input/desired output to construct the IP. For instance, hidden layers with many neurons require MI estimators with robustness toward the high dimensionality associated with such layers. MI estimators should also be able to handle convolutional layers while at the same time being computationally tractable to scale to large networks. Existing IP methods have not been able to study truly deep convolutional neural networks (CNNs). We propose an IP analysis using the new matrix-based Rényi's entropy coupled with tensor kernels, leveraging the power of kernel methods to represent properties of the probability distribution independently of the dimensionality of the data. Our results shed new light on previous studies concerning small-scale DNNs using a completely new approach. We provide a comprehensive IP analysis of large-scale CNNs, investigating the different training phases and providing new insights into the training dynamics of large-scale neural networks.
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Affiliation(s)
- Kristoffer K Wickstrøm
- Machine Learning Group, Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway
| | - Sigurd Løkse
- Machine Learning Group, Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway
| | - Michael C Kampffmeyer
- Machine Learning Group, Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway
- Norwegian Computing Center, Department of Statistical Analysis and Machine Learning, 114 Blindern, NO-0314 Oslo, Norway
| | - Shujian Yu
- Machine Learning Group, Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway
- Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
- Department of Computer Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - José C Príncipe
- Computational NeuroEngineering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Robert Jenssen
- Machine Learning Group, Department of Physics and Technology, UiT The Arctic University of Norway, NO-9037 Tromsø, Norway
- Norwegian Computing Center, Department of Statistical Analysis and Machine Learning, 114 Blindern, NO-0314 Oslo, Norway
- Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark
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Wickstrøm KK, Østmo EA, Radiya K, Mikalsen KØ, Kampffmeyer MC, Jenssen R. A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. Comput Med Imaging Graph 2023; 107:102239. [PMID: 37207397 DOI: 10.1016/j.compmedimag.2023.102239] [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: 06/06/2022] [Revised: 05/02/2023] [Accepted: 05/02/2023] [Indexed: 05/21/2023]
Abstract
Deep learning-based approaches for content-based image retrieval (CBIR) of computed tomography (CT) liver images is an active field of research, but suffer from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which limits the trustworthiness of deep CBIR systems. We address these limitations by: (1) Proposing a self-supervised learning framework that incorporates domain-knowledge into the training procedure, and, (2) by providing the first representation learning explainability analysis in the context of CBIR of CT liver images. Results demonstrate improved performance compared to the standard self-supervised approach across several metrics, as well as improved generalization across datasets. Further, we conduct the first representation learning explainability analysis in the context of CBIR, which reveals new insights into the feature extraction process. Lastly, we perform a case study with cross-examination CBIR that demonstrates the usability of our proposed framework. We believe that our proposed framework could play a vital role in creating trustworthy deep CBIR systems that can successfully take advantage of unlabeled data.
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Affiliation(s)
- Kristoffer Knutsen Wickstrøm
- Machine Learning Group at the Department of Physics and Technology, UiT the Arctic University of Norway, Tromsø NO-9037, Norway.
| | - Eirik Agnalt Østmo
- Machine Learning Group at the Department of Physics and Technology, UiT the Arctic University of Norway, Tromsø NO-9037, Norway
| | - Keyur Radiya
- Department of Gastrointestinal Surgery, University Hospital of North Norway (UNN), Tromsø, Norway
| | - Karl Øyvind Mikalsen
- Machine Learning Group at the Department of Physics and Technology, UiT the Arctic University of Norway, Tromsø NO-9037, Norway; Department of Gastrointestinal Surgery, University Hospital of North Norway (UNN), Tromsø, Norway
| | - Michael Christian Kampffmeyer
- Machine Learning Group at the Department of Physics and Technology, UiT the Arctic University of Norway, Tromsø NO-9037, Norway; Norwegian Computing Center, Department SAMBA, P.O. Box 114 Blindern, Oslo NO-0314, Norway
| | - Robert Jenssen
- Machine Learning Group at the Department of Physics and Technology, UiT the Arctic University of Norway, Tromsø NO-9037, Norway; Norwegian Computing Center, Department SAMBA, P.O. Box 114 Blindern, Oslo NO-0314, Norway; Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 København Ø, Denmark
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Luppino LT, Hansen MA, Kampffmeyer M, Bianchi FM, Moser G, Jenssen R, Anfinsen SN. Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images. IEEE Trans Neural Netw Learn Syst 2022; PP:60-72. [PMID: 35552141 DOI: 10.1109/tnnls.2022.3172183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels to the learning of the translation function. Many existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces and reduce the impact of change pixels on the learning objective. A change prior is derived in an unsupervised fashion from pixel pair affinities that are comparable across domains. To achieve code space alignment, we enforce pixels with similar affinity relations in the input domains to be correlated also in code space. We demonstrate the utility of this procedure in combination with cycle consistency. The proposed approach is compared with the state-of-the-art machine learning and deep learning algorithms. Experiments conducted on four real and representative datasets show the effectiveness of our methodology.
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Wickstrøm K, Kampffmeyer M, Mikalsen KØ, Jenssen R. Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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7
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Hansen S, Gautam S, Jenssen R, Kampffmeyer M. Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels. Med Image Anal 2022; 78:102385. [DOI: 10.1016/j.media.2022.102385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 01/20/2022] [Accepted: 02/01/2022] [Indexed: 10/19/2022]
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Boubekki A, Myhre JN, Luppino LT, Mikalsen KO, Revhaug A, Jenssen R. Clinically relevant features for predicting the severity of surgical site infections. IEEE J Biomed Health Inform 2021; 26:1794-1801. [PMID: 34665748 DOI: 10.1109/jbhi.2021.3121038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Surgical site infections are hospital-acquired infections resulting in severe risk for patients and significantly increased costs for healthcare providers. In this work, we show how to leverage irregularly sampled preoperative blood tests to predict, on the day of surgery, a future surgical site infection and its severity. Our dataset is extracted from the electronic health records of patients who underwent gastrointestinal surgery and developed either deep, shallow or no infection. We represent the patients using the concentrations of fourteen common blood components collected over the four weeks preceding the surgery partitioned into six time windows. A gradient boosting based classifier trained on our new set of features reports, respectively, an AUROC of 0:991 and 0:937 at predicting a postoperative infection and the severity thereof. Further analyses support the clinical relevance of our approach as the most important features describe the nutritional status and the liver function over the two weeks prior to surgery.
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Kuttner S, Wickstrøm KK, Lubberink M, Tolf A, Burman J, Sundset R, Jenssen R, Appel L, Axelsson J. Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function. J Cereb Blood Flow Metab 2021; 41:2229-2241. [PMID: 33557691 PMCID: PMC8392760 DOI: 10.1177/0271678x21991393] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 11/30/2020] [Accepted: 01/03/2021] [Indexed: 11/17/2022]
Abstract
Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of 15O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects.Twenty-five subjects underwent two 10 min dynamic 15O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-based AIF prediction model, considering both baseline and acetazolamide scans as training data. The MLIF approach was evaluated by comparing AIF and MLIF curves, as well as whole-brain grey matter CBF values estimated by kinetic modelling derived with either AIF or MLIF.The results showed that AIF and MLIF curves were similar and that corresponding CBF values were highly correlated and successfully differentiated before and after acetazolamide medication. In conclusion, our non-invasive MLIF method shows potential to replace the AIF obtained from blood sampling for CBF measurements using 15O-water PET and kinetic modelling.
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Affiliation(s)
- Samuel Kuttner
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
- The PET Imaging Center, University Hospital of North Norway, Tromsø, Norway
| | | | - Mark Lubberink
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - Andreas Tolf
- Department of Neuroscience, Uppsala University, Uppsala, Sweden
| | - Joachim Burman
- Department of Neuroscience, Uppsala University, Uppsala, Sweden
| | - Rune Sundset
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromsø, Norway
- The PET Imaging Center, University Hospital of North Norway, Tromsø, Norway
| | - Robert Jenssen
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Lieuwe Appel
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - Jan Axelsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
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Wickstrom K, Mikalsen KO, Kampffmeyer M, Revhaug A, Jenssen R. Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE J Biomed Health Inform 2021; 25:2435-2444. [PMID: 33284756 DOI: 10.1109/jbhi.2020.3042637] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Deep learning-based support systems have demonstrated encouraging results in numerous clinical applications involving the processing of time series data. While such systems often are very accurate, they have no inherent mechanism for explaining what influenced the predictions, which is critical for clinical tasks. However, existing explainability techniques lack an important component for trustworthy and reliable decision support, namely a notion of uncertainty. In this paper, we address this lack of uncertainty by proposing a deep ensemble approach where a collection of DNNs are trained independently. A measure of uncertainty in the relevance scores is computed by taking the standard deviation across the relevance scores produced by each model in the ensemble, which in turn is used to make the explanations more reliable. The class activation mapping method is used to assign a relevance score for each time step in the time series. Results demonstrate that the proposed ensemble is more accurate in locating relevant time steps and is more consistent across random initializations, thus making the model more trustworthy. The proposed methodology paves the way for constructing trustworthy and dependable support systems for processing clinical time series for healthcare related tasks.
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Abstract
AbstractDeep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternates between the training of a deep autoencoder and a k-means clustering of the autoencoder’s embedding. The diachronic setting, however, prevents the former to benefit from valuable information acquired by the latter. In this paper, we present an alternative where the autoencoder and the clustering are learned simultaneously. This is achieved by providing novel theoretical insight, where we show that the objective function of a certain class of Gaussian mixture models (GMM’s) can naturally be rephrased as the loss function of a one-hidden layer autoencoder thus inheriting the built-in clustering capabilities of the GMM. That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model able to jointly learn a clustering and an embedding. Experiments confirm the equivalence between the clustering module and Gaussian mixture models. Further evaluations affirm the empirical relevance of our deep architecture as it outperforms related baselines on several data sets.
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Bianchi FM, Scardapane S, Lokse S, Jenssen R. Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series. IEEE Trans Neural Netw Learn Syst 2021; 32:2169-2179. [PMID: 32598284 DOI: 10.1109/tnnls.2020.3001377] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios. Reservoir computing (RC) provides efficient tools to generate a vectorial, fixed-size representation of the MTS that can be further processed by standard classifiers. Despite their unrivaled training speed, MTS classifiers based on a standard RC architecture fail to achieve the same accuracy of fully trainable neural networks. In this article, we introduce the reservoir model space, an unsupervised approach based on RC to learn vectorial representations of MTS. Each MTS is encoded within the parameters of a linear model trained to predict a low-dimensional embedding of the reservoir dynamics. Compared with other RC methods, our model space yields better representations and attains comparable computational performance due to an intermediate dimensionality reduction procedure. As a second contribution, we propose a modular RC framework for MTS classification, with an associated open-source Python library. The framework provides different modules to seamlessly implement advanced RC architectures. The architectures are compared with other MTS classifiers, including deep learning models and time series kernels. Results obtained on the benchmark and real-world MTS data sets show that RC classifiers are dramatically faster and, when implemented using our proposed representation, also achieve superior classification accuracy.
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13
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Yu S, Wickstrom K, Jenssen R, Principe J. Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration. IEEE Trans Neural Netw Learn Syst 2021; 32:435-442. [PMID: 32071010 DOI: 10.1109/tnnls.2020.2968509] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A novel functional estimator for Rényi's α -entropy and its multivariate extension was recently proposed in terms of the normalized eigenspectrum of a Hermitian matrix of the projected data in a reproducing kernel Hilbert space (RKHS). However, the utility and possible applications of these new estimators are rather new and mostly unknown to practitioners. In this brief, we first show that this estimator enables straightforward measurement of information flow in realistic convolutional neural networks (CNNs) without any approximation. Then, we introduce the partial information decomposition (PID) framework and develop three quantities to analyze the synergy and redundancy in convolutional layer representations. Our results validate two fundamental data processing inequalities and reveal more inner properties concerning CNN training.
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Yu S, Giraldo LGS, Jenssen R, Principe JC. Multivariate Extension of Matrix-Based Rényi's α-Order Entropy Functional. IEEE Trans Pattern Anal Mach Intell 2020; 42:2960-2966. [PMID: 31395536 DOI: 10.1109/tpami.2019.2932976] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The matrix-based Rényi's α-order entropy functional was recently introduced using the normalized eigenspectrum of a Hermitian matrix of the projected data in a reproducing kernel Hilbert space (RKHS). However, the current theory in the matrix-based Rényi's α-order entropy functional only defines the entropy of a single variable or mutual information between two random variables. In information theory and machine learning communities, one is also frequently interested in multivariate information quantities, such as the multivariate joint entropy and different interactive quantities among multiple variables. In this paper, we first define the matrix-based Rényi's α-order joint entropy among multiple variables. We then show how this definition can ease the estimation of various information quantities that measure the interactions among multiple variables, such as interactive information and total correlation. We finally present an application to feature selection to show how our definition provides a simple yet powerful way to estimate a widely-acknowledged intractable quantity from data. A real example on hyperspectral image (HSI) band selection is also provided.
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16
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Kuttner S, Wickstrøm KK, Kalda G, Dorraji SE, Martin-Armas M, Oteiza A, Jenssen R, Fenton K, Sundset R, Axelsson J. Machine learning derived input-function in a dynamic 18F-FDG PET study of mice. Biomed Phys Eng Express 2020; 6:015020. [DOI: 10.1088/2057-1976/ab6496] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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17
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Wickstrøm K, Kampffmeyer M, Jenssen R. Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps. Med Image Anal 2019; 60:101619. [PMID: 31810005 DOI: 10.1016/j.media.2019.101619] [Citation(s) in RCA: 45] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 11/14/2019] [Accepted: 11/14/2019] [Indexed: 12/27/2022]
Abstract
Colorectal polyps are known to be potential precursors to colorectal cancer, which is one of the leading causes of cancer-related deaths on a global scale. Early detection and prevention of colorectal cancer is primarily enabled through manual screenings, where the intestines of a patient is visually examined. Such a procedure can be challenging and exhausting for the person performing the screening. This has resulted in numerous studies on designing automatic systems aimed at supporting physicians during the examination. Recently, such automatic systems have seen a significant improvement as a result of an increasing amount of publicly available colorectal imagery and advances in deep learning research for object image recognition. Specifically, decision support systems based on Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on both detection and segmentation of colorectal polyps. However, CNN-based models need to not only be precise in order to be helpful in a medical context. In addition, interpretability and uncertainty in predictions must be well understood. In this paper, we develop and evaluate recent advances in uncertainty estimation and model interpretability in the context of semantic segmentation of polyps from colonoscopy images. Furthermore, we propose a novel method for estimating the uncertainty associated with important features in the input and demonstrate how interpretability and uncertainty can be modeled in DSSs for semantic segmentation of colorectal polyps. Results indicate that deep models are utilizing the shape and edge information of polyps to make their prediction. Moreover, inaccurate predictions show a higher degree of uncertainty compared to precise predictions.
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Affiliation(s)
- Kristoffer Wickstrøm
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø NO-9037, Norway.
| | - Michael Kampffmeyer
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø NO-9037, Norway
| | - Robert Jenssen
- Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø NO-9037, Norway
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18
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Kampffmeyer M, Løkse S, Bianchi FM, Livi L, Salberg AB, Jenssen R. Deep divergence-based approach to clustering. Neural Netw 2019; 113:91-101. [PMID: 30798048 DOI: 10.1016/j.neunet.2019.01.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [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: 06/08/2018] [Revised: 01/14/2019] [Accepted: 01/29/2019] [Indexed: 10/27/2022]
Abstract
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of research is in its infancy, and how to design and optimize suitable loss functions to train deep neural networks for clustering is still an open question. Our contribution to this emerging field is a new deep clustering network that leverages the discriminative power of information-theoretic divergence measures, which have been shown to be effective in traditional clustering. We propose a novel loss function that incorporates geometric regularization constraints, thus avoiding degenerate structures of the resulting clustering partition. Experiments on synthetic benchmarks and real datasets show that the proposed network achieves competitive performance with respect to other state-of-the-art methods, scales well to large datasets, and does not require pre-training steps.
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Affiliation(s)
| | - Sigurd Løkse
- Machine Learning Group, UiT the Arctic University of Norway, Norway (1)
| | - Filippo M Bianchi
- Machine Learning Group, UiT the Arctic University of Norway, Norway (1)
| | - Lorenzo Livi
- Department of Computer Science, University of Exeter, UK; Departments of Computer Science and Mathematics, University of Manitoba, Canada
| | | | - Robert Jenssen
- Machine Learning Group, UiT the Arctic University of Norway, Norway (1); Norwegian Computing Center, Oslo, Norway
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Mikalsen KØ, Soguero-Ruiz C, Jensen K, Hindberg K, Gran M, Revhaug A, Lindsetmo RO, Skrøvseth SO, Godtliebsen F, Jenssen R. Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput Methods Programs Biomed 2017; 152:105-114. [PMID: 29054250 DOI: 10.1016/j.cmpb.2017.09.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [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: 07/09/2017] [Revised: 09/05/2017] [Accepted: 09/15/2017] [Indexed: 06/07/2023]
Abstract
OBJECTIVES Postoperative delirium is a common complication after major surgery among the elderly. Despite its potentially serious consequences, the complication often goes undetected and undiagnosed. In order to provide diagnosis support one could potentially exploit the information hidden in free text documents from electronic health records using data-driven clinical decision support tools. However, these tools depend on labeled training data and can be both time consuming and expensive to create. METHODS The recent learning with anchors framework resolves this problem by transforming key observations (anchors) into labels. This is a promising framework, but it is heavily reliant on clinicians knowledge for specifying good anchor choices in order to perform well. In this paper we propose a novel method for specifying anchors from free text documents, following an exploratory data analysis approach based on clustering and data visualization techniques. We investigate the use of the new framework as a way to detect postoperative delirium. RESULTS By applying the proposed method to medical data gathered from a Norwegian university hospital, we increase the area under the precision-recall curve from 0.51 to 0.96 compared to baselines. CONCLUSIONS The proposed approach can be used as a framework for clinical decision support for postoperative delirium.
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Affiliation(s)
- Karl Øyvind Mikalsen
- Department of Mathematics and Statistics, UiT The Arctic University of Norway, Tromsø, Norway; UiT Machine Learning Group, Norway.
| | - Cristina Soguero-Ruiz
- UiT Machine Learning Group, Norway; Department of Signal Theory and Comm., Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada, Spain
| | - Kasper Jensen
- Norwegian Centre for E-health Research, University Hospital of North Norway (UNN), Tromsø, Norway
| | - Kristian Hindberg
- Department of Mathematics and Statistics, UiT The Arctic University of Norway, Tromsø, Norway
| | - Mads Gran
- Department of Gastrointestinal Surgery, UNN, Tromsø, Norway
| | - Arthur Revhaug
- Department of Gastrointestinal Surgery, UNN, Tromsø, Norway; Clinic for Surgery, Cancer and Women's Health, UNN, Tromsø, Norway; Institute of Clinical Medicine, UiT, Tromsø, Norway
| | - Rolv-Ole Lindsetmo
- Department of Gastrointestinal Surgery, UNN, Tromsø, Norway; Institute of Clinical Medicine, UiT, Tromsø, Norway
| | - Stein Olav Skrøvseth
- Department of Mathematics and Statistics, UiT The Arctic University of Norway, Tromsø, Norway; Norwegian Centre for E-health Research, University Hospital of North Norway (UNN), Tromsø, Norway
| | - Fred Godtliebsen
- Department of Mathematics and Statistics, UiT The Arctic University of Norway, Tromsø, Norway
| | - Robert Jenssen
- Department of Physics and Technology, UiT, Tromsø, Norway; Norwegian Centre for E-health Research, University Hospital of North Norway (UNN), Tromsø, Norway; UiT Machine Learning Group, Norway
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Izquierdo-Verdiguier E, Laparra V, Jenssen R, Gomez-Chova L, Camps-Valls G. Optimized Kernel Entropy Components. IEEE Trans Neural Netw Learn Syst 2017; 28:1466-1472. [PMID: 26930695 DOI: 10.1109/tnnls.2016.2530403] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.
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21
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Bianchi FM, Livi L, Alippi C, Jenssen R. Multiplex visibility graphs to investigate recurrent neural network dynamics. Sci Rep 2017; 7:44037. [PMID: 28281563 PMCID: PMC5345088 DOI: 10.1038/srep44037] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Accepted: 02/01/2017] [Indexed: 11/18/2022] Open
Abstract
A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameters. Tuning them properly may be difficult and, typically, based on a trial-and-error approach. In this work, we adopt a graph-based framework to interpret and characterize internal dynamics of a class of RNNs called echo state networks (ESNs). We design principled unsupervised methods to derive hyperparameters configurations yielding maximal ESN performance, expressed in terms of prediction error and memory capacity. In particular, we propose to model time series generated by each neuron activations with a horizontal visibility graph, whose topological properties have been shown to be related to the underlying system dynamics. Successively, horizontal visibility graphs associated with all neurons become layers of a larger structure called a multiplex. We show that topological properties of such a multiplex reflect important features of ESN dynamics that can be used to guide the tuning of its hyperparamers. Results obtained on several benchmarks and a real-world dataset of telephone call data records show the effectiveness of the proposed methods.
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Affiliation(s)
- Filippo Maria Bianchi
- Machine Learning Group, Department of Physics and Technology, University of Tromsø, 9019 Tromsø, Norway
| | - Lorenzo Livi
- Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, United Kingdom
| | - Cesare Alippi
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
- Faculty of Informatics, Universitá della Svizzera Italiana, 6900 Lugano, Switzerland
| | - Robert Jenssen
- Machine Learning Group, Department of Physics and Technology, University of Tromsø, 9019 Tromsø, Norway
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Soguero-Ruiz C, Hindberg K, Mora-Jiménez I, Rojo-Álvarez JL, Skrøvseth SO, Godtliebsen F, Mortensen K, Revhaug A, Lindsetmo RO, Augestad KM, Jenssen R. Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods. J Biomed Inform 2016; 61:87-96. [PMID: 26980235 DOI: 10.1016/j.jbi.2016.03.008] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [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: 10/20/2015] [Revised: 01/27/2016] [Accepted: 03/06/2016] [Indexed: 10/22/2022]
Abstract
OBJECTIVE In this work, we have developed a learning system capable of exploiting information conveyed by longitudinal Electronic Health Records (EHRs) for the prediction of a common postoperative complication, Anastomosis Leakage (AL), in a data-driven way and by fusing temporal population data from different and heterogeneous sources in the EHRs. MATERIAL AND METHODS We used linear and non-linear kernel methods individually for each data source, and leveraging the powerful multiple kernels for their effective combination. To validate the system, we used data from the EHR of the gastrointestinal department at a university hospital. RESULTS We first investigated the early prediction performance from each data source separately, by computing Area Under the Curve values for processed free text (0.83), blood tests (0.74), and vital signs (0.65), respectively. When exploiting the heterogeneous data sources combined using the composite kernel framework, the prediction capabilities increased considerably (0.92). Finally, posterior probabilities were evaluated for risk assessment of patients as an aid for clinicians to raise alertness at an early stage, in order to act promptly for avoiding AL complications. DISCUSSION Machine-learning statistical model from EHR data can be useful to predict surgical complications. The combination of EHR extracted free text, blood samples values, and patient vital signs, improves the model performance. These results can be used as a framework for preoperative clinical decision support.
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Affiliation(s)
- Cristina Soguero-Ruiz
- Dept. of Signal Theory and Communications, Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada, Spain.
| | - Kristian Hindberg
- Dept. Mathematics and Statistics, University of Tromsø (UiT), Tromsø, Norway
| | - Inmaculada Mora-Jiménez
- Dept. of Signal Theory and Communications, Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada, Spain
| | - José Luis Rojo-Álvarez
- Dept. of Signal Theory and Communications, Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada, Spain
| | - Stein Olav Skrøvseth
- Norwegian Centre for Integrated Care and Telemedicine, Norway; University Hospital of North Norway (UNN), Norway; IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Fred Godtliebsen
- Dept. Mathematics and Statistics, University of Tromsø (UiT), Tromsø, Norway
| | - Kim Mortensen
- Dept. of Gastrointestinal Surgery, UNN, Tromsø, Norway; Institute of Clinical Medicine, UiT, Tromsø, Norway
| | - Arthur Revhaug
- Dept. of Gastrointestinal Surgery, UNN, Tromsø, Norway; Clinic for Surgery, Cancer and Women's Health, UNN, Tromsø, Norway
| | - Rolv-Ole Lindsetmo
- Dept. of Gastrointestinal Surgery, UNN, Tromsø, Norway; Institute of Clinical Medicine, UiT, Tromsø, Norway
| | - Knut Magne Augestad
- Norwegian Centre for Integrated Care and Telemedicine, Norway; Dept. of Surgery, Hammerfest Hospital, Norway; Dept. of Colorectal Surgery, University Hospitals Case Medical Center, Cleveland, USA; Institute of Clinical Medicine, UiT, Tromsø, Norway
| | - Robert Jenssen
- Norwegian Centre for Integrated Care and Telemedicine, Norway; Dept. of Physics and Technology, UiT, Tromsø, Norway
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Soguero-Ruiz C, Fei WME, Jenssen R, Augestad KM, Álvarez JLR, Jiménez IM, Lindsetmo RO, Skrøvseth SO. Data-driven Temporal Prediction of Surgical Site Infection. AMIA Annu Symp Proc 2015; 2015:1164-1173. [PMID: 26958256 PMCID: PMC4765613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Analysis of data from Electronic Health Records (EHR) presents unique challenges, in particular regarding nonuniform temporal resolution of longitudinal variables. A considerable amount of patient information is available in the EHR - including blood tests that are performed routinely during inpatient follow-up. These data are useful for the design of advanced machine learning-based methods and prediction models. Using a matched cohort of patients undergoing gastrointestinal surgery (101 cases and 904 controls), we built a prediction model for post-operative surgical site infections (SSIs) using Gaussian process (GP) regression, time warping and imputation methods to manage the sparsity of the data source, and support vector machines for classification. For most blood tests, wider confidence intervals after imputation were obtained in patients with SSI. Predictive performance with individual blood tests was maintained or improved by joint model prediction, and non-linear classifiers performed consistently better than linear models.
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Affiliation(s)
- Cristina Soguero-Ruiz
- Department of Signal Theory and Communications, Telematics and Computing, Rey Juan Carlos University, Madrid, Spain
| | - Wang M E Fei
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA
| | - Robert Jenssen
- Department of Physics and Technology, University of Tromsø - The Arctic University of Norway, Tromsø, Norway; Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway
| | - Knut Magne Augestad
- Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway; Department of Surgery, Hammerfest Hospital, Hammerfest, Norway
| | - José-Luis Rojo Álvarez
- Department of Signal Theory and Communications, Telematics and Computing, Rey Juan Carlos University, Madrid, Spain
| | - Inmaculada Mora Jiménez
- Department of Signal Theory and Communications, Telematics and Computing, Rey Juan Carlos University, Madrid, Spain
| | - Rolv-Ole Lindsetmo
- Department of Gastrointestinal Surgery, University Hospital of North Norway, Tromsø, Norway; Department of Clinical Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
| | - Stein Olav Skrøvseth
- Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway; Department of Mathematics and Statistics, University of Tromsø - The Arctic University of Norway, Tromsø, Norway
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Soguero-Ruiz C, Hindberg K, Rojo-Alvarez JL, Skrovseth SO, Godtliebsen F, Mortensen K, Revhaug A, Lindsetmo RO, Augestad KM, Jenssen R. Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records. IEEE J Biomed Health Inform 2014; 20:1404-15. [PMID: 25312965 DOI: 10.1109/jbhi.2014.2361688] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The free text in electronic health records (EHRs) conveys a huge amount of clinical information about health state and patient history. Despite a rapidly growing literature on the use of machine learning techniques for extracting this information, little effort has been invested toward feature selection and the features' corresponding medical interpretation. In this study, we focus on the task of early detection of anastomosis leakage (AL), a severe complication after elective surgery for colorectal cancer (CRC) surgery, using free text extracted from EHRs. We use a bag-of-words model to investigate the potential for feature selection strategies. The purpose is earlier detection of AL and prediction of AL with data generated in the EHR before the actual complication occur. Due to the high dimensionality of the data, we derive feature selection strategies using the robust support vector machine linear maximum margin classifier, by investigating: 1) a simple statistical criterion (leave-one-out-based test); 2) an intensive-computation statistical criterion (Bootstrap resampling); and 3) an advanced statistical criterion (kernel entropy). Results reveal a discriminatory power for early detection of complications after CRC (sensitivity 100%; specificity 72%). These results can be used to develop prediction models, based on EHR data, that can support surgeons and patients in the preoperative decision making phase.
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Jenssen R. Mean vector component analysis for visualization and clustering of nonnegative data. IEEE Trans Neural Netw Learn Syst 2013; 24:1553-1564. [PMID: 24808593 DOI: 10.1109/tnnls.2013.2262774] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Mean vector component analysis (MVCA) is introduced as a new method for visualization and clustering of nonnegative data. The method is based on dimensionality reduction by preserving the squared length, and implicitly also the direction, of the mean vector of the original data. The optimal mean vector preserving basis is obtained from the spectral decomposition of the inner-product matrix, and it is shown to capture clustering structure. MVCA corresponds to certain uncentered principal component analysis (PCA) axes. Unlike traditional PCA, these axes are in general not corresponding to the top eigenvalues. MVCA is shown to produce different visualizations and sometimes considerably improved clustering results for nonnegative data, compared with PCA.
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Jenssen R, Kloft M, Zien A, Sonnenburg S, Müller KR. A scatter-based prototype framework and multi-class extension of support vector machines. PLoS One 2012; 7:e42947. [PMID: 23118845 PMCID: PMC3484157 DOI: 10.1371/journal.pone.0042947] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2012] [Accepted: 07/15/2012] [Indexed: 11/26/2022] Open
Abstract
We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. This enables us to implement computationally efficient solvers based on sequential minimal and chunking optimization. As a further contribution, the primal problem formulation is developed in terms of regularized risk minimization and the hinge loss, revealing the score function to be used in the actual classification of test patterns. We investigate Scatter SVM properties related to generalization ability, computational efficiency, sparsity and sensitivity maps, and report promising results.
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Affiliation(s)
- Robert Jenssen
- Department of Physics and Technology, University of Tromsø, Tromsø, Norway.
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Abstract
We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising.
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Affiliation(s)
- Robert Jenssen
- Department of Physics and Technology, University of Tromsø, Tromsø, Norway.
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Jenssen R, Erdogmus D, Hild KE, Principe JC, Eltoft T. Optimizing the Cauchy-Schwarz PDF Distance for Information Theoretic, Non-parametric Clustering. Lecture Notes in Computer Science 2005. [DOI: 10.1007/11585978_3] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Jenssen R, Jenssen HL, Werner H, Köhler H. [Autoimmunity in chronic inflammatory eye disease (author's transl)]. Albrecht Von Graefes Arch Klin Exp Ophthalmol 1976; 201:193-9. [PMID: 1087842 DOI: 10.1007/bf00410071] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Preparations of allogenous and xenogenous retinal rod outer segments (ROS) were found to be adequately antigenic for detection of cellular immunity in human chorioretinitis. The electrophoretic mobility test was used for estimation of delayed-type hypersensitivity. No significant results with ROS were found in patients with panophthalmia or in control subjects. It is assumed that the results reported here in chronic ophthalmic inflammation may be produced by or connected with autoimmune processes.
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Mehlan J, Eichler J, Jenssen R. [Retinal venous occlusions (author's transl)]. Klin Monbl Augenheilkd 1974; 165:785-96. [PMID: 4468999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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32
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Jenssen R, Bostelmann I. [Experience with the preoperative use of faustan]. Z Arztl Fortbild (Jena) 1973; 67:768-9. [PMID: 4771929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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33
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Eichler J, Jenssen R, Mehlan J. [Arterial occlusion of the retina]. Z Arztl Fortbild (Jena) 1973; 67:674-8. [PMID: 4148960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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34
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Pietruschka G, Jenssen R. [Coincidence of diabetes mellitus and eye diseases]. Dtsch Gesundheitsw 1971; 26:635-40. [PMID: 5576694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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35
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Dässler CG, Jenssen R. [The ACTH test in normal and pathological early and late pregnancy]. Endokrinologie 1971; 57:289-96. [PMID: 4325992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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