Multiview Long-Short Spatial Contrastive Learning For 3D Medical Image Analysis

Cao, Gongpeng; Wang, Yiping; Zhang, Manli; Zhang, Jing; Kang, Guixia; Xu, Xiaoyan

doi:10.1109/icassp43922.2022.9747913

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Cited by 3 publications

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“… 40 , 41 , 42 , 43 In the biomedical field, coupling different types of images is a common practice. Classification of breast tumor 44 and Alzheimer’s disease (AD) 45 and the fusion of learning strategies based on different views of the same image 46 are some examples.…”

Section: Introductionmentioning

confidence: 99%

MERGE: A model for multi-input biomedical federated learning

Casella,

Riviera,

Aldinucci

et al. 2023

Patterns

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Section: Introductionmentioning

confidence: 99%

MERGE: A model for multi-input biomedical federated learning

Casella,

Riviera,

Aldinucci

et al. 2023

Patterns

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Graph-Based Alzheimer’s Disease Diagnosis with Contrastive Learning and Graph Transformer

Zhang,

Cao,

et al. 2023

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

VanBerlo,

Hoey,

Wong

2024

BMC Med Imaging

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Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.

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Multiview Long-Short Spatial Contrastive Learning For 3D Medical Image Analysis

Cited by 3 publications

References 11 publications

MERGE: A model for multi-input biomedical federated learning

MERGE: A model for multi-input biomedical federated learning

Graph-Based Alzheimer’s Disease Diagnosis with Contrastive Learning and Graph Transformer

A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

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