Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement

External validation of the prediction model of intradialytic hypotension: a multicenter prospective cohort study

OBJECTIVE

Intradialytic hypotension (IDH) is a common and serious complication in patients with Maintenance Hemodialysis (MHD). The purpose of this study is to externally verify three IDH risk prediction models recently developed by Ma et al. and recalibrate, update and present the optimal model to improve the accuracy and applicability of the model in clinical environment.

METHODS

A multicenter prospective cohort study of patients from 11 hemodialysis centers in Sichuan Province, China, was conducted using convenience sampling from March 2022 to July 2022, with a follow-up period of 1 month. Model performance was assessed by: (1) Discrimination: Evaluated through the computation of the Area Under Curve (AUC) and its corresponding 95% confidence intervals. (2) Calibration: scrutinized through visual inspection of the calibration plot and utilization of the Brier score. (3) The incremental value of risk prediction and the utility of updating the model were gauged using NRI (Net Reclassification Improvement) and IDI (Integrated Discrimination Improvement). Decision Curve Analysis (DCA) was employed to evaluate the clinical benefit of updating the model.

RESULTS

The final cohort comprised 2235 individuals undergoing maintenance hemodialysis, exhibiting a 14.6% occurrence rate of IDH. The externally validated Area Under the Curve (AUC) values for the three original prediction models were 0.746 (95% CI: 0.718 to 0.775), 0.709 (95% CI: 0.679 to 0.739), and 0.735 (95% CI: 0.706 to 0.764) respectively. Conversely, the AUC value for the recalibrated and updated columnar plot model reached 0.817 (95% CI: 0.791 to 0.842), accompanied by a Brier score of 0.081. Furthermore, Decision Curve Analysis (DCA) exhibited a net benefit within the threshold probability range of 15.2% to 87.1%.

CONCLUSION

Externally validated, recalibrated, updated, and presented IDH prediction models may serve as a valuable instrument for evaluating IDH risk in clinical practice. Furthermore, they hold the potential to guide clinical providers in discerning individuals at risk and facilitating judicious clinical intervention decisions.

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients' quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored to utilize machine learning to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS. A database of patient-, tumor- and surgery-specific features was constructed via retrospective chart review of 242 consecutive patients who underwent microsurgical resection of VS over a 7-year study period. This database was then used to train non-linear supervised machine learning classifiers to predict facial nerve preservation, defined as House-Brackmann (HB) I vs. facial nerve injury, defined as HB II-VI, as determined at 6-month outpatient follow-up. A random forest algorithm demonstrated 90.5% accuracy, 90% sensitivity and 90% specificity in facial nerve injury prognostication. A random variable (rv) was generated by randomly sampling a Gaussian distribution and used as a benchmark to compare the predictiveness of other features. This analysis revealed age, body mass index (BMI), case length and the tumor dimension representing tumor growth towards the brainstem as prognosticators of facial nerve injury. When validated via prospective assessment of facial nerve injury risk, this model demonstrated 84% accuracy. Here, we describe the development of a machine learning algorithm to predict the likelihood of facial nerve injury following microsurgical resection of VS. In addition to serving as a clinically applicable tool, this highlights the potential of machine learning to reveal non-linear relationships between variables which may have clinical value in prognostication of outcomes for high-risk surgical procedures.

An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans

BACKGROUND

Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-constrained settings. This study aims to propose an efficient diagnostic deep learning model specifically designed for the classification of intracranial hemorrhage in brain CT scans.

METHOD

Our proposed model utilizes a combination of depthwise separable convolutions and a multi-receptive field mechanism to achieve a trade-off between performance and computational efficiency. The model was trained on RSNA datasets and validated on CQ500 dataset and PhysioNet dataset.

RESULT

Through a comprehensive comparison with state-of-the-art models, our model achieves an average AUROC score of 0.952 on RSNA datasets and exhibits robust generalization capabilities, comparable to SE-ResNeXt, across other open datasets. Furthermore, the parameter count of our model is just 3 % of that of MobileNet V3.

CONCLUSION

This study presents a diagnostic deep-learning model that is optimized for classifying intracranial hemorrhages in brain CT scans. The efficient characteristics make our proposed model highly promising for broader applications in medical settings.

MACG-Net: Multi-axis cross gating network for deformable medical image registration

Deformable Image registration is a fundamental yet vital task for preoperative planning, intraoperative information fusion, disease diagnosis and follow-ups. It solves the non-rigid deformation field to align an image pair. Latest approaches such as VoxelMorph and TransMorph compute features from a simple concatenation of moving and fixed images. However, this often leads to weak alignment. Moreover, the convolutional neural network (CNN) or the hybrid CNN-Transformer based backbones are constrained to have limited sizes of receptive field and cannot capture long range relations while full Transformer based approaches are computational expensive. In this paper, we propose a novel multi-axis cross grating network (MACG-Net) for deformable medical image registration, which combats these limitations. MACG-Net uses a dual stream multi-axis feature fusion module to capture both long-range and local context relationships from the moving and fixed images. Cross gate blocks are integrated with the dual stream backbone to consider both independent feature extractions in the moving-fixed image pair and the relationship between features from the image pair. We benchmark our method on several different datasets including 3D atlas-based brain MRI, inter-patient brain MRI and 2D cardiac MRI. The results demonstrate that the proposed method has achieved state-of-the-art performance. The source code has been released at https://github.com/Valeyards/MACG.

Efficient skin lesion segmentation with boundary distillation

Medical image segmentation models are commonly known for their complex structures, which often render them impractical for use on edge computing devices and compromising efficiency in the segmentation process. In light of this, the industry has proposed the adoption of knowledge distillation techniques. Nevertheless, the vast majority of existing knowledge distillation methods are focused on the classification tasks of skin diseases. Specifically, for the segmentation tasks of dermoscopy lesion images, these knowledge distillation methods fail to fully recognize the importance of features in the boundary regions of lesions within medical images, lacking boundary awareness for skin lesions. This paper introduces pioneering medical image knowledge distillation architecture. The aim of this method is to facilitate the efficient transfer of knowledge from existing complex medical image segmentation networks to a more simplified student network. Initially, a masked boundary feature (MBF) distillation module is designed. By applying random masking to the periphery of skin lesions, the MBF distillation module obliges the student network to reproduce the comprehensive features of the teacher network. This process, in turn, augments the representational capabilities of the student network. Building on the MBF distillation module, this paper employs a cascaded combination approach to integrate the MBF distillation module into a multi-head boundary feature (M2BF) distillation module, further strengthening the student network's feature learning ability and enhancing the overall image segmentation performance of the distillation model. This method has been experimentally validated on the public datasets ISIC-2016 and PH2, with results showing significant performance improvements in the student network. Our findings highlight the practical utility of the lightweight network distilled using our approach, particularly in scenarios demanding high operational speed and minimal storage usage. This research offers promising prospects for practical applications in the realm of medical image segmentation.

Linear semantic transformation for semi-supervised medical image segmentation

Medical image segmentation is a focus research and foundation in developing intelligent medical systems. Recently, deep learning for medical image segmentation has become a standard process and succeeded significantly, promoting the development of reconstruction, and surgical planning of disease diagnosis. However, semantic learning is often inefficient owing to the lack of supervision of feature maps, resulting in that high-quality segmentation models always rely on numerous and accurate data annotations. Learning robust semantic representation in latent spaces remains a challenge. In this paper, we propose a novel semi-supervised learning framework to learn vital attributes in medical images, which constructs generalized representation from diverse semantics to realize medical image segmentation. We first build a self-supervised learning part that achieves context recovery by reconstructing space and intensity of medical images, which conduct semantic representation for feature maps. Subsequently, we combine semantic-rich feature maps and utilize simple linear semantic transformation to convert them into image segmentation. The proposed framework was tested using five medical segmentation datasets. Quantitative assessments indicate the highest scores of our method on IXI (73.78%), ScaF (47.50%), COVID-19-Seg (50.72%), PC-Seg (65.06%), and Brain-MR (72.63%) datasets. Finally, we compared our method with the latest semi-supervised learning methods and obtained 77.15% and 75.22% DSC values, respectively, ranking first on two representative datasets. The experimental results not only proved that the proposed linear semantic transformation was effectively applied to medical image segmentation, but also presented its simplicity and ease-of-use to pursue robust segmentation in semi-supervised learning. Our code is now open at: https://github.com/QingYunA/Linear-Semantic-Transformation-for-Semi-Supervised-Medical-Image-Segmentation.

DP-GAN+B: A lightweight generative adversarial network based on depthwise separable convolutions for generating CT volumes

X-rays, commonly used in clinical settings, offer advantages such as low radiation and cost-efficiency. However, their limitation lies in the inability to distinctly visualize overlapping organs. In contrast, Computed Tomography (CT) scans provide a three-dimensional view, overcoming this drawback but at the expense of higher radiation doses and increased costs. Hence, from both the patient's and hospital's standpoints, there is substantial medical and practical value in attempting the reconstruction from two-dimensional X-ray images to three-dimensional CT images. In this paper, we introduce DP-GAN+B as a pioneering approach for transforming two-dimensional frontal and lateral lung X-rays into three-dimensional lung CT volumes. Our method innovatively employs depthwise separable convolutions instead of traditional convolutions and introduces vector and fusion loss for superior performance. Compared to prior models, DP-GAN+B significantly reduces the generator network parameters by 21.104 M and the discriminator network parameters by 10.82 M, resulting in a total reduction of 31.924 M (44.17%). Experimental results demonstrate that our network can effectively generate clinically relevant, high-quality CT images from X-ray data, presenting a promising solution for enhancing diagnostic imaging while mitigating cost and radiation concerns.

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

In the current research study, a new method is presented to diagnose Anterior Cruciate Ligament (ACL) tears by introducing an optimized version of the InceptionV4 model. Our proposed methodology utilizes a custom-made variant of the Snow Leopard Optimization Algorithm, known as the Fractional-order Snow Leopard Optimization Algorithm (FO-LOA), to extract essential features from knee magnetic resonance imaging (MRI) images. This results in a substantial improvement in the accuracy of ACL tear detection. By effectively extracting critical features from knee MRI images, our proposed methodology significantly enhances diagnostic accuracy, potentially reducing false negatives and false positives. The enhanced model based on FO-LOA underwent thorough testing using the MRNet dataset, demonstrating exceptional performance metrics including an accuracy rate of 98.00%, sensitivity of 98.00%, precision of 97.00%, specificity of 98.00%, F1-score of 98.00%, and Matthews Correlation Coefficient (MCC) of 88.00%. These findings surpass current methodologies like Convolutional Neural Network (CNN), Inception-v3, Deep Belief Networks and Improved Honey Badger Algorithm (DBN/IHBA), integration of the CNN with an Amended Cooking Training-based Optimizer version (CNN/ACTO), Self-Supervised Representation Learning (SSRL), signifying a significant breakthrough in ACL injury diagnosis. Using FO-SLO to optimize the InceptionV4 framework shows promise in improving the accuracy of ACL tear identification, enabling prompt and efficient treatment interventions.

Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions

The work presented in this paper is in the area of brain tumor detection. We propose a fast detection system with 3D MRI scans of Flair modality. It performs 2 functions, predicting the gray level distribution and location distribution of the pixels in the tumor regions and generating tumor masks with pixel-wise precision. To facilitate 3D data analysis and processing, we introduce a 2D histogram presentation encompassing the gray-level distribution and pixel-location distribution of a 3D object. In the proposed system, specific 2D histograms highlighting tumor-related features are established by exploiting the left-right asymmetry of a brain structure. A modulation function, generated from the input data of each patient case, is applied to the 2D histograms to transform them into coarsely or finely predicted distributions of tumor pixels. The prediction result helps to identify/remove tumor-free slices. The prediction and removal operations are performed to the axial, coronal and sagittal slice series of a brain image, transforming it into a 3D minimum bounding box of its tumor region. The bounding box is utilized to finalize the prediction and generate a 3D tumor mask. The proposed system has been tested extensively with the data of more than 1200 patient cases in BraTS2018∼2021 datasets. The test results demonstrate that the predicted 2D histograms resemble closely the true ones. The system delivers also very good tumor detection results, comparable to those of state-of-the-art CNN systems with mono-modality inputs. They are reproducible and obtained at an extremely low computation cost and without need for training.

GrMoNAS: A granularity-based multi-objective NAS framework for efficient medical diagnosis

Neural Architecture Search (NAS) has been widely applied to automate medical image diagnostics. However, traditional NAS methods require significant computational resources and time for performance evaluation. To address this, we introduce the GrMoNAS framework, designed to balance diagnostic accuracy and efficiency using proxy datasets for granularity transformation and multi-objective optimization algorithms. The approach initiates with a coarse granularity phase, wherein diverse candidate neural architectures undergo evaluation utilizing a reduced proxy dataset. This initial phase facilitates the swift and effective identification of architectures exhibiting promise. Subsequently, in the fine granularity phase, a comprehensive validation and optimization process is undertaken for these identified architectures. Concurrently, employing multi-objective optimization and Pareto frontier sorting aims to enhance both accuracy and computational efficiency simultaneously. Importantly, the GrMoNAS framework is particularly suitable for hospitals with limited computational resources. We evaluated GrMoNAS in a range of medical scenarios, such as COVID-19, Skin cancer, Lung, Colon, and Acute Lymphoblastic Leukemia diseases, comparing it against traditional models like VGG16, VGG19, and recent NAS approaches including GA-CNN, EBNAS, NEXception, and CovNAS. The results show that GrMoNAS achieves comparable or superior diagnostic precision, significantly enhancing diagnostic efficiency. Moreover, GrMoNAS effectively avoids local optima, indicating its significant potential for precision medical diagnosis.

Comparing the osteogenesis outcomes of different lumbar interbody fusions (A/O/X/T/PLIF) by evaluating their mechano-driven fusion processes

BACKGROUND

In lumbar interbody fusion (LIF), achieving proper fusion status requires osteogenesis to occur in the disc space. Current LIF techniques, including anterior, oblique, lateral, transforaminal, and posterior LIF (A/O/X/T/PLIF), may result in varying osteogenesis outcomes due to differences in biomechanical characteristics.

METHODS

A mechano-regulation algorithm was developed to predict the fusion processes of A/O/X/T/PLIF based on finite element modeling and iterative evaluations of the mechanobiological activities of mesenchymal stem cells (MSCs) and their differentiated cells (osteoblasts, chondrocytes, and fibroblasts). Fusion occurred in the grafting region, and each differentiated cell type generated the corresponding tissue proportional to its concentration. The corresponding osteogenesis volume was calculated by multiplying the osteoblast concentration by the grafting volume.

RESULTS

TLIF and ALIF achieved markedly greater osteogenesis volumes than did PLIF and O/XLIF (5.46, 5.12, 4.26, and 3.15 cm3, respectively). Grafting volume and cage size were the main factors influencing the osteogenesis outcome in patients treated with LIF. A large grafting volume allowed more osteoblasts (bone tissues) to be accommodated in the disc space. A small cage size reduced the cage/endplate ratio and therefore decreased the stiffness of the LIF. This led to a larger osteogenesis region to promote osteoblastic differentiation of MSCs and osteoblast proliferation (bone regeneration), which subsequently increased the bone fraction in the grafting space.

CONCLUSION

TLIF and ALIF produced more favorable biomechanical environments for osteogenesis than did PLIF and O/XLIF. A small cage and a large grafting volume improve osteogenesis by facilitating osteogenesis-related cell activities driven by mechanical forces.

Digital healthcare framework for patients with disabilities based on deep federated learning schemes

Utilizing digital healthcare services for patients who use wheelchairs is a vital and effective means to enhance their healthcare. Digital healthcare integrates various healthcare facilities, including local laboratories and centralized hospitals, to provide healthcare services for individuals in wheelchairs. In digital healthcare, the Internet of Medical Things (IoMT) allows local wheelchairs to connect with remote digital healthcare services and generate sensors from wheelchairs to monitor and process healthcare. Recently, it has been observed that wheelchair patients, when older than thirty, suffer from high blood pressure, heart disease, body glucose, and others due to less activity because of their disabilities. However, existing wheelchair IoMT applications are straightforward and do not consider the healthcare of wheelchair patients with their diseases during their disabilities. This paper presents a novel digital healthcare framework for patients with disabilities based on deep-federated learning schemes. In the proposed framework, we offer the federated learning deep convolutional neural network schemes (FL-DCNNS) that consist of different sub-schemes. The offloading scheme collects the sensors from integrated wheelchair bio-sensors as smartwatches such as blood pressure, heartbeat, body glucose, and oxygen. The smartwatches worked with wearable devices for disabled patients in our framework. We present the federated learning-enabled laboratories for data training and share the updated weights with the data security to the centralized node for decision and prediction. We present the decision forest for centralized healthcare nodes to decide on aggregation with the different constraints: cost, energy, time, and accuracy. We implemented a deep CNN scheme in each laboratory to train and validate the model locally on the node with the consideration of resources. Simulation results show that FL-DCNNS obtained the optimal results on the sensor data and minimized the energy by 25%, time 19%, cost 28%, and improved the accuracy of disease prediction by 99% as compared to existing digital healthcare schemes for wheelchair patients.

XGraphCDS: An explainable deep learning model for predicting drug sensitivity from gene pathways and chemical structures

Cancer is a highly complex disease characterized by genetic and phenotypic heterogeneity among individuals. In the era of precision medicine, understanding the genetic basis of these individual differences is crucial for developing new drugs and achieving personalized treatment. Despite the increasing abundance of cancer genomics data, predicting the relationship between cancer samples and drug sensitivity remains challenging. In this study, we developed an explainable graph neural network framework for predicting cancer drug sensitivity (XGraphCDS) based on comparative learning by integrating cancer gene expression information and drug chemical structure knowledge. Specifically, XGraphCDS consists of a unified heterogeneous network and multiple sub-networks, with molecular graphs representing drugs and gene enrichment scores representing cell lines. Experimental results showed that XGraphCDS consistently outperformed most state-of-the-art baselines (R2 = 0.863, AUC = 0.858). We also constructed a separate in vivo prediction model by using transfer learning strategies with in vitro experimental data and achieved good predictive power (AUC = 0.808). Simultaneously, our framework is interpretable, providing insights into resistance mechanisms alongside accurate predictions. The excellent performance of XGraphCDS highlights its immense potential in aiding the development of selective anti-tumor drugs and personalized dosing strategies in the field of precision medicine.

An embedded feature selection method based on generalized classifier neural network for cancer classification

The selection of relevant genes plays a vital role in classifying high-dimensional microarray gene expression data. Sparse group Lasso and its variants have been employed for gene selection to capture the interactions of genes within a group. Most of the embedded methods are linear sparse learning models that fail to capture the non-linear interactions. Additionally, very less attention is given to solving multi-class problems. The existing methods create overlapping groups, which further increases dimensionality. The paper proposes a neural network-based embedded feature selection method that can represent the non-linear relationship. In an effort toward an explainable model, a generalized classifier neural network (GCNN) is adopted as the model for the proposed embedded feature selection. GCNN has well-defined architecture in terms of the number of layers and neurons within each layer. Each layer has a distinct functionality, eliminating the obscure nature of most neural networks. The paper proposes a feature selection approach called Weighted GCNN (WGCNN) that embeds feature weighting as a part of training the neural network. Since the gene expression data comprises a large number of features, to avoid overfitting of the model a statistical guided dropout is implemented at the input layer. The proposed method works for binary as well as multi-class classification problems likewise. Experimental validation is carried out on seven microarray datasets on three learning models and compared with six state-of-art methods that are popularly employed for feature selection. The WGCNN performs well in terms of the F1 score and the number of features selected.

Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, highlighting the need for early and accurate detection. In this study, we proposed DeepDrAMD, a hierarchical vision transformer-based deep learning model that integrates data augmentation techniques and SwinTransformer, to detect AMD and distinguish between different subtypes using color fundus photographs (CFPs). The DeepDrAMD was trained on the in-house WMUEH training set and achieved high performance in AMD detection with an AUC of 98.76% in the WMUEH testing set and 96.47% in the independent external Ichallenge-AMD cohort. Furthermore, the DeepDrAMD effectively classified dryAMD and wetAMD, achieving AUCs of 93.46% and 91.55%, respectively, in the WMUEH cohort and another independent external ODIR cohort. Notably, DeepDrAMD excelled at distinguishing between wetAMD subtypes, achieving an AUC of 99.36% in the WMUEH cohort. Comparative analysis revealed that the DeepDrAMD outperformed conventional deep-learning models and expert-level diagnosis. The cost-benefit analysis demonstrated that the DeepDrAMD offers substantial cost savings and efficiency improvements compared to manual reading approaches. Overall, the DeepDrAMD represents a significant advancement in AMD detection and differential diagnosis using CFPs, and has the potential to assist healthcare professionals in informed decision-making, early intervention, and treatment optimization.

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images

Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to giga-pixel WSI classification problems, current MIL models are often incapable of differentiating a WSI with extremely small tumor lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the attention mechanism from properly weighting the areas corresponding to minor tumor lesions. To overcome this challenge, we propose salient instance inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification. We introduce a novel representation learning for histopathology images to identify representative normal keys. These keys facilitate the selection of salient instances within WSIs, forming bags with high tumor-to-normal ratios. Finally, an attention mechanism is employed for slide-level classification based on formed bags. Our results show that salient instance inference can improve the tumor-to-normal area ratio in the tumor WSIs. As a result, SiiMIL achieves 0.9225 AUC and 0.7551 recall on the Camelyon16 dataset, which outperforms the existing MIL models. In addition, SiiMIL can generate tumor-sensitive attention heatmaps that is more interpretable to pathologists than the widely used attention-based MIL method. Our experiments imply that SiiMIL can accurately identify tumor instances, which could only take up less than 1% of a WSI, so that the ratio of tumor to normal instances within a bag can increase by two to four times.

Trends and hotspots in research on medical images with deep learning: a bibliometric analysis from 2013 to 2023

Background

With the rapid development of the internet, the improvement of computer capabilities, and the continuous advancement of algorithms, deep learning has developed rapidly in recent years and has been widely applied in many fields. Previous studies have shown that deep learning has an excellent performance in image processing, and deep learning-based medical image processing may help solve the difficulties faced by traditional medical image processing. This technology has attracted the attention of many scholars in the fields of computer science and medicine. This study mainly summarizes the knowledge structure of deep learning-based medical image processing research through bibliometric analysis and explores the research hotspots and possible development trends in this field.

Methods

Retrieve the Web of Science Core Collection database using the search terms "deep learning," "medical image processing," and their synonyms. Use CiteSpace for visual analysis of authors, institutions, countries, keywords, co-cited references, co-cited authors, and co-cited journals.

Results

The analysis was conducted on 562 highly cited papers retrieved from the database. The trend chart of the annual publication volume shows an upward trend. Pheng-Ann Heng, Hao Chen, and Klaus Hermann Maier-Hein are among the active authors in this field. Chinese Academy of Sciences has the highest number of publications, while the institution with the highest centrality is Stanford University. The United States has the highest number of publications, followed by China. The most frequent keyword is "Deep Learning," and the highest centrality keyword is "Algorithm." The most cited author is Kaiming He, and the author with the highest centrality is Yoshua Bengio.

Conclusion

The application of deep learning in medical image processing is becoming increasingly common, and there are many active authors, institutions, and countries in this field. Current research in medical image processing mainly focuses on deep learning, convolutional neural networks, classification, diagnosis, segmentation, image, algorithm, and artificial intelligence. The research focus and trends are gradually shifting toward more complex and systematic directions, and deep learning technology will continue to play an important role.

Visual attribution using Adversarial Latent Transformations

The ability to accurately locate all indicators of disease within medical images is vital for comprehending the effects of the disease, as well as for weakly-supervised segmentation and localization of the diagnostic correlators of disease. Existing methods either use classifiers to make predictions based on class-salient regions or else use adversarial learning based image-to-image translation to capture such disease effects. However, the former does not capture all relevant features for visual attribution (VA) and are prone to data biases; the latter can generate adversarial (misleading) and inefficient solutions when dealing in pixel values. To address this issue, we propose a novel approach Visual Attribution using Adversarial Latent Transformations (VA2LT). Our method uses adversarial learning to generate counterfactual (CF) normal images from abnormal images by finding and modifying discrepancies in the latent space. We use cycle consistency between the query and CF latent representations to guide our training. We evaluate our method on three datasets including a synthetic dataset, the Alzheimer's Disease Neuroimaging Initiative dataset, and the BraTS dataset. Our method outperforms baseline and related methods on all datasets.

Hierarchical Harris hawks optimizer for feature selection

INTRODUCTION

The main feature selection methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must include a swarm intelligence algorithm, and its performance in feature selection is closely related to the algorithm's quality. Therefore, it is essential to choose and design a suitable algorithm to improve the performance of the feature selection method based on the wrapper. Harris hawks optimization (HHO) is a superb optimization approach that has just been introduced. It has a high convergence rate and a powerful global search capability but it has an unsatisfactory optimization effect on high dimensional problems or complex problems. Therefore, we introduced a hierarchy to improve HHO's ability to deal with complex problems and feature selection.

OBJECTIVES

To make the algorithm obtain good accuracy with fewer features and run faster in feature selection, we improved HHO and named it EHHO. On 30 UCI datasets, the improved HHO (EHHO) can achieve very high classification accuracy with less running time and fewer features.

METHODS

We first conducted extensive experiments on 23 classical benchmark functions and compared EHHO with many state-of-the-art metaheuristic algorithms. Then we transform EHHO into binary EHHO (bEHHO) through the conversion function and verify the algorithm's ability in feature extraction on 30 UCI data sets.

RESULTS

Experiments on 23 benchmark functions show that EHHO has better convergence speed and minimum convergence than other peers. At the same time, compared with HHO, EHHO can significantly improve the weakness of HHO in dealing with complex functions. Moreover, on 30 datasets in the UCI repository, the performance of bEHHO is better than other comparative optimization algorithms.

CONCLUSION

Compared with the original bHHO, bEHHO can achieve excellent classification accuracy with fewer features and is also better than bHHO in running time.

Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter

Lung cancer creates pulmonary nodules in the patient's lung, which may be diagnosed early on using computer-aided diagnostics. A novel automated pulmonary nodule diagnosis technique using three-dimensional deep convolutional neural networks and multi-layered filter has been presented in this paper. For the suggested automated diagnosis of lung nodule, volumetric computed tomographic images are employed. The proposed approach generates three-dimensional feature layers, which retain the temporal links between adjacent slices of computed tomographic images. The use of several activation functions at different levels of the proposed network results in increased feature extraction and efficient classification. The suggested approach divides lung volumetric computed tomography pictures into malignant and benign categories. The suggested technique's performance is evaluated using three commonly used datasets in the domain: LUNA 16, LIDC-IDRI, and TCIA. The proposed method outperforms the state-of-the-art in terms of accuracy, sensitivity, specificity, F-1 score, false-positive rate, false-negative rate, and error rate.

Diagnosis of Parkinson's disease based on voice signals using SHAP and hard voting ensemble method

Parkinson's disease (PD) is the second most common progressive neurological condition after Alzheimer's. The significant number of individuals afflicted with this illness makes it essential to develop a method to diagnose the conditions in their early phases. PD is typically identified from motor symptoms or via other Neuroimaging techniques. Expensive, time-consuming, and unavailable to the general public, these methods are not very accurate. Another issue to be addressed is the black-box nature of machine learning methods that needs interpretation. These issues encourage us to develop a novel technique using Shapley additive explanations (SHAP) and Hard Voting Ensemble Method based on voice signals to diagnose PD more accurately. Another purpose of this study is to interpret the output of the model and determine the most important features in diagnosing PD. The present article uses Pearson Correlation Coefficients to understand the relationship between input features and the output. Input features with high correlation are selected and then classified by the Extreme Gradient Boosting, Light Gradient Boosting Machine, Gradient Boosting, and Bagging. Moreover, the weights in Hard Voting Ensemble Method are determined based on the performance of the mentioned classifiers. At the final stage, it uses SHAP to determine the most important features in PD diagnosis. The effectiveness of the proposed method is validated using 'Parkinson Dataset with Replicated Acoustic Features' from the UCI machine learning repository. It has achieved an accuracy of 85.42%. The findings demonstrate that the proposed method outperformed state-of-the-art approaches and can assist physicians in diagnosing Parkinson's cases.

A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study

The Sine Cosine Algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA's fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine's (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy.

Automatic base-model selection for white blood cell image classification using meta-learning

Healthcare has benefited from the implementation of deep-learning models to solve medical image classification tasks. For example, White Blood Cell (WBC) image analysis is used to diagnose different pathologies like leukemia. However, medical datasets are mostly imbalanced, inconsistent, and costly to collect. Hence, it is difficult to select an adequate model to overcome the mentioned drawbacks. Therefore, we propose a novel methodology to automatically select models to solve WBC classification tasks. These tasks contain images collected using different staining methods, microscopes, and cameras. The proposed methodology includes meta- and base-level learnings. At the meta-level, we implemented meta-models based on prior-models to acquire meta-knowledge by solving meta-tasks using the shades of gray color constancy method. To determine the best models to solve new WBC tasks we developed an algorithm that uses the meta-knowledge and the Centered Kernel Alignment metric. Next, a learning rate finder method is employed to adapt the selected models. The adapted models (base-models) are used in an ensemble learning approach achieving accuracy and balanced accuracy scores of 98.29 and 97.69 in the Raabin dataset; 100 in the BCCD dataset; 99.57 and 99.51 in the UACH dataset, respectively. The results in all datasets outperform most of the state-of-the-art models, which demonstrates our methodology's advantage of automatically selecting the best model to solve WBC tasks. The findings also indicate that our methodology can be extended to other medical image classification tasks where is difficult to select an adequate deep-learning model to solve new tasks with imbalanced, limited, and out-of-distribution data.

Dense Dilated Multi-Scale Supervised Attention-Guided Network for histopathology image segmentation

Over the last couple of decades, the introduction and proliferation of whole-slide scanners led to increasing interest in the research of digital pathology. Although manual analysis of histopathological images is still the gold standard, the process is often tedious and time consuming. Furthermore, manual analysis also suffers from intra- and interobserver variability. Separating structures or grading morphological changes can be difficult due to architectural variability of these images. Deep learning techniques have shown great potential in histopathology image segmentation that drastically reduces the time needed for downstream tasks of analysis and providing accurate diagnosis. However, few algorithms have clinical implementations. In this paper, we propose a new deep learning model Dense Dilated Multiscale Supervised Attention-Guided (D2MSA) Network for histopathology image segmentation that makes use of deep supervision coupled with a hierarchical system of novel attention mechanisms. The proposed model surpasses state-of-the-art performance while using similar computational resources. The performance of the model has been evaluated for the tasks of gland segmentation and nuclei instance segmentation, both of which are clinically relevant tasks to assess the state and progress of malignancy. Here, we have used histopathology image datasets for three different types of cancer. We have also performed extensive ablation tests and hyperparameter tuning to ensure the validity and reproducibility of the model performance. The proposed model is available at www.github.com/shirshabose/D2MSA-Net.

FRSR: Framework for real-time scene reconstruction in robot-assisted minimally invasive surgery

3D reconstruction of the intra-operative scenes provides precise position information which is the foundation of various safety related applications in robot-assisted surgery, such as augmented reality. Herein, a framework integrated into a known surgical system is proposed to enhance the safety of robotic surgery. In this paper, we present a scene reconstruction framework to restore the 3D information of the surgical site in real time. In particular, a lightweight encoder-decoder network is designed to perform disparity estimation, which is the key component of the scene reconstruction framework. The stereo endoscope of da Vinci Research Kit (dVRK) is adopted to explore the feasibility of the proposed approach, and it provides the possibility for the migration to other Robot Operating System (ROS) based robot platforms due to the strong independence on hardware. The framework is evaluated using three different scenarios, including a public dataset (3018 pairs of endoscopic images), the scene from the dVRK endoscope in our lab as well as a self-made clinical dataset captured from an oncology hospital. Experimental results show that the proposed framework can reconstruct 3D surgical scenes in real time (25 FPS), and achieve high accuracy (2.69 ± 1.48 mm in MAE, 5.47 ± 1.34 mm in RMSE and 0.41 ± 0.23 in SRE, respectively). It demonstrates that our framework can reconstruct intra-operative scenes with high reliability of both accuracy and speed, and the validation of clinical data also shows its potential in surgery. This work enhances the state of art in 3D intra-operative scene reconstruction based on medical robot platforms. The clinical dataset has been released to promote the development of scene reconstruction in the medical image community.

Recent progress in transformer-based medical image analysis

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.

PPNet: Pyramid pooling based network for polyp segmentation

Colonoscopy is the gold standard method for investigating the gastrointestinal tract. Localizing the polyps in colonoscopy images plays a vital role when doing a colonoscopy screening, and it is also quite important for the following treatment, e.g., polyp resection. Many deep learning-based methods have been applied for solving the polyp segmentation issue. However, precisely polyp segmentation is still an open issue. Considering the effectiveness of the Pyramid Pooling Transformer (P2T) in modeling long-range dependencies and capturing robust contextual features, as well as the power of pyramid pooling in extracting features, we propose a pyramid pooling based network for polyp segmentation, namely PPNet. We first adopt the P2T as the encoder for extracting more powerful features. Next, a pyramid feature fusion module (PFFM) combining the channel attention scheme is utilized for learning a global contextual feature, in order to guide the information transition in the decoder branch. Aiming to enhance the effectiveness of PPNet on feature extraction during the decoder stage layer by layer, we introduce the memory-keeping pyramid pooling module (MPPM) into each side branch of the encoder, and transmit the corresponding feature to each lower-level side branch. Experimental results conducted on five public colorectal polyp segmentation datasets are given and discussed. Our method performs better compared with several state-of-the-art polyp extraction networks, which demonstrate the effectiveness of the mechanism of pyramid pooling for colorectal polyp segmentation.

Modeling the survival of colorectal cancer patients based on colonoscopic features in a feature ensemble vision transformer

The prognosis of patients with colorectal cancer (CRC) mostly relies on the classic tumor node metastasis (TNM) staging classification. A more accurate and convenient prediction model would provide a better prognosis and assist in treatment. From May 2014 to December 2017, patients who underwent an operation for CRC were enrolled. The proposed feature ensemble vision transformer (FEViT) used ensemble classifiers to benefit the combinations of relevant colonoscopy features from the pretrained vision transformer and clinical features, including sex, age, family history of CRC, and tumor location, to establish the prognostic model. A total of 1729 colonoscopy images were enrolled in the current retrospective study. For the prediction of patient survival, FEViT achieved an accuracy of 94 % with an area under the receiver operating characteristic curve of 0.93, which was better than the TNM staging classification (90 %, 0.83) in the experiment. FEViT reduced the limited receptive field and gradient disappearance in the conventional convolutional neural network and was a relatively effective and efficient procedure. The promising accuracy of FEViT in modeling survival makes the prognosis of CRC patients more predictable and practical.

Sparse-to-dense coarse-to-fine depth estimation for colonoscopy

Colonoscopy, as the golden standard for screening colon cancer and diseases, offers considerable benefits to patients. However, it also imposes challenges on diagnosis and potential surgery due to the narrow observation perspective and limited perception dimension. Dense depth estimation can overcome the above limitations and offer doctors straightforward 3D visual feedback. To this end, we propose a novel sparse-to-dense coarse-to-fine depth estimation solution for colonoscopic scenes based on the direct SLAM algorithm. The highlight of our solution is that we utilize the scattered 3D points obtained from SLAM to generate accurate and dense depth in full resolution. This is done by a deep learning (DL)-based depth completion network and a reconstruction system. The depth completion network effectively extracts texture, geometry, and structure features from sparse depth along with RGB data to recover the dense depth map. The reconstruction system further updates the dense depth map using a photometric error-based optimization and a mesh modeling approach to reconstruct a more accurate 3D model of colons with detailed surface texture. We show the effectiveness and accuracy of our depth estimation method on near photo-realistic challenging colon datasets. Experiments demonstrate that the strategy of sparse-to-dense coarse-to-fine can significantly improve the performance of depth estimation and smoothly fuse direct SLAM and DL-based depth estimation into a complete dense reconstruction system.

MFEFNet: Multi-scale feature enhancement and Fusion Network for polyp segmentation

The polyp segmentation technology based on computer-aided can effectively avoid the deterioration of polyps and prevent colorectal cancer. To segment the polyp target precisely, the Multi-Scale Feature Enhancement and Fusion Network (MFEFNet) is proposed. First of all, to balance the network's predictive ability and complexity, ResNet50 is designed as the backbone network, and the Shift Channel Block (SCB) is used to unify the spatial location of feature mappings and emphasize local information. Secondly, to further improve the network's feature-extracting ability, the Feature Enhancement Block (FEB) is added, which decouples features, reinforces features by multiple perspectives and reconstructs features. Meanwhile, to weaken the semantic gap in the feature fusion process, we propose strong associated couplers, the Multi-Scale Feature Fusion Block (MSFFB) and the Reducing Difference Block (RDB), which are mainly composed of multiple cross-complementary information interaction modes and reinforce the long-distance dependence between features. Finally, to further refine local regions, the Polarized Self-Attention (PSA) and the Balancing Attention Module (BAM) are introduced for better exploration of detailed information between foreground and background boundaries. Experiments have been conducted under five benchmark datasets (Kvasir-SEG, CVC-ClinicDB, CVC-ClinicDB, CVC300 and CVC-ColonDB) and compared with state-of-the-art polyp segmentation algorithms. The experimental result shows that the proposed network improves Dice and mean intersection over union (mIoU) by an average score of 3.4% and 4%, respectively. Therefore, extensive experiments demonstrate that the proposed network performs favorably against more than a dozen state-of-the-art methods on five popular polyp segmentation benchmarks.

Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.

BCU-Net: Bridging ConvNeXt and U-Net for medical image segmentation

Medical image segmentation enables doctors to observe lesion regions better and make accurate diagnostic decisions. Single-branch models such as U-Net have achieved great progress in this field. However, the complementary local and global pathological semantics of heterogeneous neural networks have not yet been fully explored. The class-imbalance problem remains a serious issue. To alleviate these two problems, we propose a novel model called BCU-Net, which leverages the advantages of ConvNeXt in global interaction and U-Net in local processing. We propose a new multilabel recall loss (MRL) module to relieve the class imbalance problem and facilitate deep-level fusion of local and global pathological semantics between the two heterogeneous branches. Extensive experiments were conducted on six medical image datasets including retinal vessel and polyp images. The qualitative and quantitative results demonstrate the superiority and generalizability of BCU-Net. In particular, BCU-Net can handle diverse medical images with diverse resolutions. It has a flexible structure owing to its plug-and-play characteristics, which promotes its practicality.

RBS-Net: Hippocampus segmentation using multi-layer feature learning with the region, boundary and structure loss

Hippocampus has great influence over the Alzheimer's disease (AD) research because of its essential role as a biomarker in the human brain. Thus the performance of hippocampus segmentation influences the development of clinical research for brain disorders. Deep learning using U-net-like networks becomes prevalent in hippocampus segmentation on Magnetic Resonance Imaging (MRI) due to its efficiency and accuracy. However, current methods lose sufficient detailed information during pooling, which hinders the segmentation results. And weak supervision on the details like edges or positions results in fuzzy and coarse boundary segmentation, causing great differences between the segmentation and ground-truth. In view of these drawbacks, we propose a Region-Boundary and Structure Net (RBS-Net), which consists of a primary net and an auxiliary net. (1) Our primary net focuses on the region distribution of hippocampus and introduces a distance map for boundary supervision. Furthermore the primary net adds a multi-layer feature learning module to compensate the information loss during pooling and strengthen the differences between the foreground and background, improving the region and boundary segmentation. (2) The auxiliary net concentrates on the structure similarity and also utilizes the multi-layer feature learning module, and this parallel task can refine encoders by similarizing the structure of the segmentation and ground-truth. We train and test our network using 5-fold cross-validation on HarP, a public available hippocampus dataset. Experimental results demonstrate that our proposed RBS-Net achieves a Dice of 89.76% in average, outperforming several state-of-the-art hippocampus segmentation methods. Furthermore, in few shot circumstances, our proposed RBS-Net achieves better results in terms of a comprehensive evaluation compared to several state-of-the-art deep learning-based methods. Finally we can observe that visual segmentation results for the boundary and detailed regions are improved by our proposed RBS-Net.

Morphological feature recognition of different differentiation stages of induced ADSCs based on deep learning

In order to accurately identify the morphological features of different differentiation stages of induced Adipose Derived Stem Cells (ADSCs) and judge the differentiation types of induced ADSCs, a morphological feature recognition method of different differentiation stages of induced ADSCs based on deep learning is proposed. Using the super-resolution image acquisition method of ADSCs differentiation based on stimulated emission depletion imaging, after obtaining the super-resolution images at different stages of inducing ADSCs differentiation, the noise of the obtained image is removed and the image quality is optimized through the ADSCs differentiation image denoising model based on low rank nonlocal sparse representation; The denoised image is taken as the recognition target of the morphological feature recognition method for ADSCs differentiation image based on the improved Visual Geometry Group (VGG-19) convolutional neural network. Through the improved VGG-19 convolutional neural network and class activation mapping method, the morphological feature recognition and visual display of the recognition results at different stages of inducing ADSCs differentiation are realized. After testing, this method can accurately identify the morphological features of different differentiation stages of induced ADSCs, and is available.

Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning

BACKGROUND AND OBJECTIVES

The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, this task suffers from the "insufficient and incomplete" data issue since it is clinically impossible to have adequate training samples for one patient at each day. Besides, the training samples are extremely imbalanced since the patients who will progress to the severe stage is far less than those who will not progress to the non-severe stage.

METHOD

We consider the severity prediction of pulmonary diseases as a time estimation problem based on CT scans. To handle the issue of "insufficient and incomplete" training samples, we introduced label distribution learning (LDL). Specifically, we generate a label distribution for each patient, making a CT image contribute to not only the learning of its chronological day, but also the learning of its neighboring days. In addition, a cost-sensitive mechanism is introduced to explore the imbalance data issue. To identify the importance of pulmonary segments in pulmonary disease severity prediction, multi-kernel learning in composite kernel space is further incorporated and particle swarm optimization (PSO) is used to find the optimal kernel weights.

RESULTS

We compare the performance of the proposed CS-LD-MKSVR algorithm with several classical machine learning algorithms and deep learning (DL) algorithms. The proposed method has obtained the best classification results on the in-house data, fully indicating its effectiveness in pulmonary disease severity prediction.

CONTRIBUTIONS

The severity prediction of pulmonary diseases is considered as a time estimation problem, and label distribution is introduced to describe the conversion time from non-severe stage to severe stage. The cost-sensitive mechanism is also introduced to handle the data imbalance issue to further improve the classification performance.

Comprehensive analysis of cuproptosis-related genes in immune infiltration and prognosis in lung adenocarcinoma

Copper-dependent cell death, called cuproptosis, is connected to tumor development, prognosis, and the immune response. Nevertheless, the function of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of lung adenocarcinoma (LUAD) remains unknown. This work used R software packages to classify the raw data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases of LUAD patients. Afterward, the connections of the various subgroups, clinical pathological traits, and immune infiltration (IMIF) features with the TME mutation status were explored. Ultimately, a nomogram and calibration curve were developed, aiming at enhancing the clinical application of CRG scores and estimating the survival probability of patients. Moreover, the relationships between cuproptosis and the molecular traits, immune cell infiltration of tumor tissue, prognosis, and clinical treatment of patients were investigated in this work. Subsequently, the CRG score was established to predict overall survival (OS), and its credible predictive ability in LUAD patients was identified. Afterward, a highly credible nomogram was created to contribute to the clinical viability of the CRG score. Furthermore, as demonstrated, gene signatures could be applied in assessing tumor immune cell infiltration, clinical traits, and prognosis. In addition, high tumor mutation burden, immunological activity, and significant survival probability were characterized by low CRG scores, and high CRG scores were related to immunosuppression and stromal pathway activation. The current work also discovered a predictive CRG-related signature for LUAD patients, probably contributing to TME trait clarification and more potent immunotherapy strategy exploration.

Spatio-temporal deep forest for emotion recognition based on facial electromyography signals

Emotion recognition is a key component of human-computer interaction technology, for which facial electromyogram (fEMG) is an important physiological modality. Recently, deep-learning-based emotion recognition using fEMG signals has drawn increased attention. However, the ability of effective feature extraction and the demand of large-scale training data are two dominant factors that restrict the performance of emotion recognition. In this paper, a novel spatio-temporal deep forest (STDF) model is proposed to classify three categories of discrete emotions (neutral, sadness, and fear) using multi-channel fEMG signals. The feature extraction module fully extracts effective spatio-temporal features of fEMG signals using a combination of 2D frame sequences and multi-grained scanning. Meanwhile, a cascade forest-based classifier is designed to provide optimal structures for different scales of training data via automatically adjusting the number of cascade layers. The proposed model and five comparison methods were evaluated on our in-house fEMG dataset that included three discrete emotions and three channels of fEMG electrodes with a total of twenty-seven subjects. Experimental results demonstrate that the proposed STDF model achieves the best recognition performance with an average accuracy of 97.41%. Besides, our proposed STDF model can reduced the scale of training data to 50% while the average accuracy of emotion recognition is only reduced by about 5%. Our proposed model offers an effective solution for practical applications of fEMG-based emotion recognition.

FRCNN-AA-CIF: An automatic detection model of colon polyps based on attention awareness and context information fusion

It is noted that the foreground and background of the polyp images detected under colonoscopy are not highly differentiated, and the feature map extracted by common deep learning object detection models keep getting smaller as the number of networks increases. Therefore, these models tend to ignore the details in pictures, resulting in a high polyp missed detection rate. To reduce the missed detection rate, this paper proposes an automatic detection model of colon polyps based on attention awareness and context information fusion (FRCNN-AA-CIF) based on a two-stage object detection model Faster Region-Convolutional Neural Network (FR-CNN). First, since the addition of attention awareness can make the feature extraction network pay more attention to polyp features, we propose an attention awareness module based on Squeeze-and-Excitation Network (SENet) and Efficient Channel Attention Module (ECA-Net) and add it after each block of the backbone network. Specifically, we first use the 1*1 convolution of ECA-Net to extract local cross-channel information and then use the two fully connected layers of SENet to reduce and increase the dimension, to filter out the channels that are more useful for feature learning. Further, because of the presence of air bubbles, impurities, inflammation, and accumulation of digestive matter around polyps, we used context information around polyps to enhance the focus on polyp features. In particular, after the network extracts the region of interest, we fuse the region of interest with its context information to improve the detection rate of polyps. The proposed model was tested on the colonoscopy dataset provided by Huashan Hospital. Numerical experiments show that FRCNN-AA-CIF has the highest detection accuracy (mAP of 0.817), the lowest missed detection rate of 4.22%, and the best classification effect (AUC of 95.98%). Its mAP increased by 3.3%, MDR decreased by 1.97%, and AUC increased by 1.8%. Compared with other object detection models, FRCNN-AA-CIF has significantly improved recognition accuracy and reduced missed detection rate.

Unsupervised anomaly detection in brain MRI: Learning abstract distribution from massive healthy brains

PURPOSE

To develop a general unsupervised anomaly detection method based only on MR images of normal brains to automatically detect various brain abnormalities.

MATERIALS AND METHODS

In this study, a novel method based on three-dimensional deep autoencoder network is proposed to automatically detect and segment various brain abnormalities without being trained on any abnormal samples. A total of 578 normal T2w MR volumes without obvious abnormalities were used for model training and validation. The proposed 3D autoencoder was evaluated on two different datasets (BraTs dataset and in-house dataset) containing T2w volumes from patients with glioblastoma, multiple sclerosis and cerebral infarction. Lesions detection and segmentation performance were reported as AUC, precision-recall curve, sensitivity, and Dice score.

RESULTS

In anomaly detection, AUCs for three typical lesions were as follows: glioblastoma, 0.844; multiple sclerosis, 0.858; cerebral infarction, 0.807. In anomaly segmentation, the mean Dice for glioblastomas was 0.462. The proposed network also has the ability to generate an anomaly heatmap for visualization purpose.

CONCLUSION

Our proposed method was able to automatically detect various brain anomalies such as glioblastoma, multiple sclerosis, and cerebral infarction. This work suggests that unsupervised anomaly detection is a powerful approach to detect arbitrary brain abnormalities without labeled samples. It has the potential to support diagnostic workflow in radiology as an automated tool for computer-aided image analysis.

Genomic instability-related twelve-microRNA signatures for predicting the prognosis of gastric cancer

Gastric cancer (GC) ranks fifth among all malignant tumors globally, especially in East Asia, and has attracted extensive attention and research. MicroRNA (miRNA) modulation during genomic instability (GI) may be associated with the development and metastasis of malignant tumors. We aimed to identify GI-related miRNA signatures for the prediction of GC prognosis. We constructed a GI-related miRNA signature (GIMiSig) scheme based on The Cancer Genome Atlas (TCGA) training set (n = 389), which was later verified based on the TCGA test set (n = 194). GI-related miRNAs were identified by analyzing somatic mutation profiles and miRNA expression. A GI-related miRNA-gene co-expression network was also constructed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed to reveal possible biological pathways associated with GI-related miRNAs. The correlation of the GIMiSig with clinical factors of the TCGA dataset was analyzed. MiRNA mimics and inhibitors were used to evaluate the biological functions of miR-100-5p and miR-145-3p in GC cell lines AGS and MKN-45. This study identified a GI-related 12-miRNA signature for the prediction of GC prognosis. GIMiSig scores, similar to tumor stages, showed significant correlations with overall survival (OS, p < 0.05). GIMiSig showed high accuracy in predicting GC prognosis. MiR-100-5p and miR-145-3p promoted cell growth, invasion, and migration but inhibited apoptosis in GC cells. We report a reliable GI-related 12-miRNA signature for predicting GC prognosis. Furthermore, miR-100-5p and miR-145-3p may promote GC cell growth, invasion, and migration.

Twist-Net: A multi-modality transfer learning network with the hybrid bilateral encoder for hypopharyngeal cancer segmentation

Hypopharyngeal cancer (HPC) is a rare disease. Therefore, it is a challenge to automatically segment HPC tumors and metastatic lymph nodes (HPC risk areas) from medical images with the small-scale dataset. Combining low-level details and high-level semantics from feature maps in different scales can improve the accuracy of segmentation. Herein, we propose a Multi-Modality Transfer Learning Network with Hybrid Bilateral Encoder (Twist-Net) for Hypopharyngeal Cancer Segmentation. Specifically, we propose a Bilateral Transition (BT) block and a Bilateral Gather (BG) block to twist (fuse) high-level semantic feature maps and low-level detailed feature maps. We design a block with multi-receptive field extraction capabilities, M Block, to capture multi-scale information. To avoid overfitting caused by the small scale of the dataset, we propose a transfer learning method that can transfer priors experience from large computer vision datasets to multi-modality medical imaging datasets. Compared with other methods, our method outperforms other methods on HPC dataset, achieving the highest Dice of 82.98%. Our method is also superior to other methods on two public medical segmentation datasets, i.e., the CHASE_DB1 dataset and BraTS2018 dataset. On these two datasets, the Dice of our method is 79.83% and 84.87%, respectively. The code is available at: https://github.com/zhongqiu1245/TwistNet.

OrbitNet-A fully automated orbit multi-organ segmentation model based on transformer in CT images

The delineation of orbital organs is a vital step in orbital diseases diagnosis and preoperative planning. However, an accurate multi-organ segmentation is still a clinical problem which suffers from two limitations. First, the contrast of soft tissue is relatively low. It usually cannot clearly show the boundaries of organs. Second, the optic nerve and the rectus muscle are difficult to distinguish because they are spatially adjacent and have similar geometry. To address these challenges, we propose the OrbitNet model to automatically segment orbital organs in CT images. Specifically, we present a global feature extraction module based on the transformer architecture called FocusTrans encoder, which enhance the ability to extract boundary features. To make the network focus on the extraction of edge features in the optic nerve and rectus muscle, the SA block is used to replace the convolution block in the decoding stage. In addition, we use the structural similarity measure (SSIM) loss as a part of the hybrid loss function to learn the edge differences of the organs better. OrbitNet has been trained and tested on the CT dataset collected by the Eye Hospital of Wenzhou Medical University. The experimental results show that our proposed model achieved superior results. The average Dice Similarity Coefficient (DSC) is 83.9%, the value of average 95% Hausdorff Distance (HD95) is 1.62 mm, and the value of average Symmetric Surface Distance (ASSD) is 0.47 mm. Our model also has good performance on the MICCAI 2015 challenge dataset.

A deep network using coarse clinical prior for myopic maculopathy grading

Pathological Myopia (PM) is a globally prevalent eye disease which is one of the main causes of blindness. In the long-term clinical observation, myopic maculopathy is a main criterion to diagnose PM severity. The grading of myopic maculopathy can provide a severity and progression prediction of PM to perform treatment and prevent myopia blindness in time. In this paper, we propose a feature fusion framework to utilize tessellated fundus and the brightest region in fundus images as prior knowledge. The proposed framework consists of prior knowledge extraction module and feature fusion module. Prior knowledge extraction module uses traditional image processing methods to extract the prior knowledge to indicate coarse lesion positions in fundus images. Furthermore, the prior, tessellated fundus and the brightest region in fundus images, are integrated into deep learning network as global and local constrains respectively by feature fusion module. In addition, rank loss is designed to increase the continuity of classification score. We collect a private color fundus dataset from Beijing TongRen Hospital containing 714 clinical images. The dataset contains all 5 grades of myopic maculopathy which are labeled by experienced ophthalmologists. Our framework achieves 0.8921 five-grade accuracy on our private dataset. Pathological Myopia (PALM) dataset is used for comparison with other related algorithms. Our framework is trained with 400 images and achieves an AUC of 0.9981 for two-class grading. The results show that our framework can achieve a good performance for myopic maculopathy grading.

Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation

Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student's prediction. The combined outputs of the students' prediction with the teachers' correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT.

FDTrans: Frequency Domain Transformer Model for predicting subtypes of lung cancer using multimodal data

BACKGROUND AND PURPOSE

Accurate identification of lung cancer subtypes in medical images is of great significance for the diagnosis and treatment of lung cancer. Despite substantial progress in existing methods, they remain challenging due to limited annotated datasets, large intra-class differences, and high inter-class similarities.

METHODS

To address these challenges, we propose a Frequency Domain Transformer Model (FDTrans) to identify patients' lung cancer subtypes using the TCGA lung cancer dataset. We add a pre-processing process to transfer histopathological images to the frequency domain using a block-based discrete cosine transform and design a coordinate Coordinate-Spatial Attention Module (CSAM) to obtain critical detail information by reassigning weights to the location information and channel information of different frequency vectors. Then, a Cross-Domain Transformer Block (CDTB) is designed for Y, Cb, and Cr channel features, capturing the long-term dependencies and global contextual connections between different component features. At the same time, feature extraction is performed on the genomic data to obtain specific features. Finally, the image branch and the gene branch are fused, and the classification result is output through the fully connected layer.

RESULTS

In 10-fold cross-validation, the method achieves an AUC of 93.16% and overall accuracy of 92.33%, which is better than similar current lung cancer subtypes classification detection methods.

CONCLUSION

This method can help physicians diagnose the subtypes classification of lung cancer in patients and can benefit from both spatial and frequency domain information.

Deep multimodal graph-based network for survival prediction from highly multiplexed images and patient variables

The spatial architecture of the tumour microenvironment and phenotypic heterogeneity of tumour cells have been shown to be associated with cancer prognosis and clinical outcomes, including survival. Recent advances in highly multiplexed imaging, including imaging mass cytometry (IMC), capture spatially resolved, high-dimensional maps that quantify dozens of disease-relevant biomarkers at single-cell resolution, that contain potential to inform patient-specific prognosis. Existing automated methods for predicting survival, on the other hand, typically do not leverage spatial phenotype information captured at the single-cell level. Furthermore, there is no end-to-end method designed to leverage the rich information in whole IMC images and all marker channels, and aggregate this information with clinical data in a complementary manner to predict survival with enhanced accuracy. To that end, we present a deep multimodal graph-based network (DMGN) with two modules: (1) a multimodal graph-based module that considers relationships between spatial phenotype information in all image regions and all clinical variables adaptively, and (2) a clinical embedding module that automatically generates embeddings specialised for each clinical variable to enhance multimodal aggregation. We demonstrate that our modules are consistently effective at improving survival prediction performance using two public breast cancer datasets, and that our new approach can outperform state-of-the-art methods in survival prediction.

Semi-XctNet: Volumetric images reconstruction network from a single projection image via semi-supervised learning

Deep learning networks have achieved remarkable progress in various tasks of medical imaging. Most of the recent success in computer vision highly depend on large amounts of carefully annotated data, whereas labelling is arduous, time-consuming and in need of expertise. In this paper, a semi-supervised learning method, Semi-XctNet, is proposed for volumetric images reconstruction from a single X-ray image. In our framework, the effect of regularization on pixel-level prediction is enhanced by introducing a transformation consistent strategy into the model. Furthermore, a multi-stage training strategy is designed to ameliorate the generalization performance of the teacher network. An assistant module is also introduced to improve the pixel quality of pseudo-labels, thereby further improving the reconstruction accuracy of the semi-supervised model. The semi-supervised method proposed in this paper has been extensively validated on the LIDC-IDRI lung cancer detection public data set. Quantitative results show that SSIM (structural similarity measurement) and PSNR (peak signal noise ratio) are 0.8384 and 28.7344 respectively. Compared with the state-of-the-arts, Semi-XctNet exhibits excellent reconstruction performance, thus demonstrating the effectiveness of our method on the task of volumetric images reconstruction network from a single X-ray image.

RA-DENet: Reverse Attention and Distractions Elimination Network for polyp segmentation

To address the problems of polyps of different shapes, sizes, and colors, low-contrast polyps, various noise distractions, and blurred edges on colonoscopy, we propose the Reverse Attention and Distraction Elimination Network, which includes Improved Reverse Attention, Distraction Elimination, and Feature Enhancement. First, we input the images in the polyp image set, and use the five levels polyp features and the global polyp feature extracted from the Res2Net-based backbone as the input of the Improved Reverse Attention to obtain augmented representations of salient and non-salient regions to capture the different shapes of polyp and distinguish low-contrast polyps from background. Then, the augmented representations of salient and non-salient areas are fed into the Distraction Elimination to obtain the refined polyp feature without false positive and false negative distractions for eliminating noises. Finally, the extracted low-level polyp feature is used as the input of the Feature Enhancement to obtain the edge feature for supplementing missing edge information of polyp. The polyp segmentation result is output by connecting the edge feature with the refined polyp feature. The proposed method is evaluated on five polyp datasets and compared with the current polyp segmentation models. Our model improves the mDice to 0.760 on the most challenge dataset (ETIS).