Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism

Ding, Yuhan; Xie, Weifang; Liao, Zhifang; Wong, Kelvin Kian Loong

doi:10.1016/j.cmpb.2022.107041

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…To address the complexities of disentangled learning and self-ensembling within CheXpert [78] binary classification, Gyawali et al [79] integrated a temporal ensemble alongside an unsupervised variational auto-encoder (VAE). Previous studies [80,81] employed the disentangled representation M1 obtained from an unsupervised VAE as an outline for a subsequently developed VAE-based semi-supervised framework, often termed the M1 + M2 model. The authors [79] sought to refine the M1 + M2 model by substituting M2 for a self-ensembling SSL network and incorporating a temporal ensemble on unsupervised targets to promote agreement among ensemble predictions.…”

Section: Temporal Ensemblementioning

confidence: 99%

A Critical Analysis of Deep Semi-Supervised Learning Approaches for Enhanced Medical Image Classification

Shakya,

Alavi,

Porteous

et al. 2024

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Deep semi-supervised learning (DSSL) is a machine learning paradigm that blends supervised and unsupervised learning techniques to improve the performance of various models in computer vision tasks. Medical image classification plays a crucial role in disease diagnosis, treatment planning, and patient care. However, obtaining labeled medical image data is often expensive and time-consuming for medical practitioners, leading to limited labeled datasets. DSSL techniques aim to address this challenge, particularly in various medical image tasks, to improve model generalization and performance. DSSL models leverage both the labeled information, which provides explicit supervision, and the unlabeled data, which can provide additional information about the underlying data distribution. That offers a practical solution to resource-intensive demands of data annotation, and enhances the model’s ability to generalize across diverse and previously unseen data landscapes. The present study provides a critical review of various DSSL approaches and their effectiveness and challenges in enhancing medical image classification tasks. The study categorized DSSL techniques into six classes: consistency regularization method, deep adversarial method, pseudo-learning method, graph-based method, multi-label method, and hybrid method. Further, a comparative analysis of performance for six considered methods is conducted using existing studies. The referenced studies have employed metrics such as accuracy, sensitivity, specificity, AUC-ROC, and F1 score to evaluate the performance of DSSL methods on different medical image datasets. Additionally, challenges of the datasets, such as heterogeneity, limited labeled data, and model interpretability, were discussed and highlighted in the context of DSSL for medical image classification. The current review provides future directions and considerations to researchers to further address the challenges and take full advantage of these methods in clinical practices.

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Section: Temporal Ensemblementioning

confidence: 99%

A Critical Analysis of Deep Semi-Supervised Learning Approaches for Enhanced Medical Image Classification

Shakya,

Alavi,

Porteous

et al. 2024

Information

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“…In the era of deep learning in health care management [ 5 , 6 ], classification [ 7 , 8 ] and segmentation of cardiac MR images (CMRI) has drawn a lot of attention [ 9 – 22 ]. Various semi-automatic and automatic cardiac segmentation methods have been developed.…”

Section: Introductionmentioning

confidence: 99%

“…Various semi-automatic and automatic cardiac segmentation methods have been developed. Early segmentation methods employed semi-automatic segmentation approaches such as those presented in the work of Ding et al [ 9 ], Sharan et al [ 10 ] and Decourt et al [ 11 ]. Semi-automatic methods necessitate significant user intervention, as a result, they are unsuitable for applications requiring rapid segmentation.…”

Section: Introductionmentioning

confidence: 99%

CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation

Shoieb,

Fathalla,

Youssef

et al. 2023

Phys Eng Sci Med

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Cardiac image segmentation is a critical step in the early detection of cardiovascular disease. The segmentation of the biventricular is a prerequisite for evaluating cardiac function in cardiac magnetic resonance imaging (CMRI). In this paper, a cascaded model CAT-Seg is proposed for segmentation of 3D-CMRI volumes. CAT-Seg addresses the problem of biventricular confusion with other regions and localized the region of interest (ROI) to reduce the scope of processing. A modified DeepLabv3+ variant integrating SqueezeNet (SqueezeDeepLabv3+) is proposed as a part of CAT-Seg. SqueezeDeepLabv3+ handles the different shapes of the biventricular through the different cardiac phases, as the biventricular only accounts for small portion of the volume slices. Also, CAT-Seg presents a segmentation approach that integrates attention mechanisms into 3D Residual UNet architecture (3D-ResUNet) called 3D-ARU to improve the segmentation results of the three major structures (left ventricle (LV), Myocardium (Myo), and right ventricle (RV)). The integration of the spatial attention mechanism into ResUNet handles the fuzzy edges of the three structures. The proposed model achieves promising results in training and testing with the Automatic Cardiac Diagnosis Challenge (ACDC 2017) dataset and the external validation using MyoPs. CAT-Seg demonstrates competitive performance with state-of-the-art models. On ACDC 2017, CAT-Seg is able to segment LV, Myo, and RV with an average minimum dice symmetry coefficient (DSC) performance gap of 1.165%, 4.36%, and 3.115% respectively. The average maximum improvement in terms of DSC in segmenting LV, Myo and RV is 4.395%, 6.84% and 7.315% respectively. On MyoPs external validation, CAT-Seg outperformed the state-of-the-art in segmenting LV, Myo, and RV with an average minimum performance gap of 6.13%, 5.44%, and 2.912% respectively.

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A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

Liu,

Kainth,

Zhou

et al. 2024

NMR in Biomedicine

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Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical‐level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state‐of‐the‐art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self‐supervised learning, generative models, few‐shot learning, and semi‐supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field—including emerging algorithms, such as contrastive language‐image pretraining, and potential combinations across the methods discussed—that can further increase the efficacy of image segmentation with limited labels.

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Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism

Cited by 5 publications

References 32 publications

A Critical Analysis of Deep Semi-Supervised Learning Approaches for Enhanced Medical Image Classification

A Critical Analysis of Deep Semi-Supervised Learning Approaches for Enhanced Medical Image Classification

CAT-Seg: cascaded medical assistive tool integrating residual attention mechanisms and Squeeze-Net for 3D MRI biventricular segmentation

A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

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