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A Review of Predictive and Contrastive Self-supervised Learning for Medical Images
Wei-Chien Wang1; Euijoon Ahn2; Dagan Feng1; Jinman Kim1
Source PublicationMachine Intelligence Research
ISSN2731-538X
2023
Volume20Issue:4Pages:483-513
AbstractOver the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the application of deep learning in medical image analysis is limited by the scarcity of high-quality annotated medical imaging data. An emerging solution is self-supervised learning (SSL), among which contrastive SSL is the most successful approach to rivalling or outperforming supervised learning. This review investigates several state-of-the-art contrastive SSL algorithms originally on natural images as well as their adaptations for medical images, and concludes by discussing recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain.
KeywordSelf-supervised learning (SSL), contrastive learning, deep learning, medical image analysis, computer vision
DOI10.1007/s11633-022-1406-4
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Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55991
Collection学术期刊_Machine Intelligence Research
Affiliation1.Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Computer Science, The University of Sydney, Sydney NSW 2006, Australia
2.College of Science and Engineering, James Cook University, Cairns QLD 4811, Australia
Recommended Citation
GB/T 7714
Wei-Chien Wang,Euijoon Ahn,Dagan Feng,et al. A Review of Predictive and Contrastive Self-supervised Learning for Medical Images[J]. Machine Intelligence Research,2023,20(4):483-513.
APA Wei-Chien Wang,Euijoon Ahn,Dagan Feng,&Jinman Kim.(2023).A Review of Predictive and Contrastive Self-supervised Learning for Medical Images.Machine Intelligence Research,20(4),483-513.
MLA Wei-Chien Wang,et al."A Review of Predictive and Contrastive Self-supervised Learning for Medical Images".Machine Intelligence Research 20.4(2023):483-513.
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