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A Review of Predictive and Contrastive Self-supervised Learning for Medical Images

  • Review
  • Open Access
  • Published: 03 June 2023
  • volume 20, pages 483–513 (2023)
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Machine Intelligence Research Aims and scope Submit manuscript
A Review of Predictive and Contrastive Self-supervised Learning for Medical Images
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  • Wei-Chien Wang  ORCID: orcid.org/0000-0002-3255-72121,
  • Euijoon Ahn  ORCID: orcid.org/0000-0001-7027-067X2,
  • Dagan Feng  ORCID: orcid.org/0000-0002-3381-214X1 &
  • …
  • Jinman Kim  ORCID: orcid.org/0000-0001-5960-10601 

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Abstract

Over 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.

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Authors and Affiliations

  1. Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia

    Wei-Chien Wang, Dagan Feng & Jinman Kim

  2. College of Science and Engineering, James Cook University, Cairns, QLD, 4811, Australia

    Euijoon Ahn

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Correspondence to Wei-Chien Wang.

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Wei-Chien Wang received the B.Sc. degree in mechatronic engineering from Huafan University, China in 2006, and the M. Sc. degree in manufacturing information and systems from the Cheng Kung University, China in 2008. She is currently a Ph. D. degree candidate in computer science at Biomedical Data Analysis and Visualisation (BDAV), School of Computer Science, The University of Sydney, Australia. She was a full-time research assistant at the Taiwan Normal University, China from 2009 to 2010. She worked as a software engineer at Hi-Lo System Research Co., Ltd., China from 2010 to 2011, and at the Software Design Center, Foxconn International Holdings, Ltd., Foxconn Technology Group, and the FIH Taiwan Design Center, Hon Hai Precision Industry Co., Ltd., China between 2011 and 2012. Since 2013, she has been a research student in Australia, working on various projects in deep learning and computer vision. She has also been a visiting researcher and lecturer with the Penghu University of Science and Technology, China since 2021.

Her research interests include visual deep learning and artificial intelligence of things (AIoT), she now focuses on self-supervised learning for medical image analysis.

Euijoon Ahn received the B. Eng. degree in information technology from the University of Newcastle, Australia in 2009, and the M. Eng. degree in information technology and the M.Phil, degree in computer science from University of Sydney, Australia in 2014 and 2016, respectively. He received the Ph.D. degree in computer science from The University of Sydney, Australia in 2020. He is a lecturer at the College of Science and Engineering, James Cook University, Australia. Prior to this, he was a postdoctoral research fellow at the Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Computer Science, The University of Sydney, Australia.

Dr. Ahn is a member of SICE, IEE, and IEEE. He produced top-tier publications in computer vision and medical image computing, including papers in IEEE T-MI, T-BME, JBHI, MedIA, PR, CVPR, AAAI and MICCAI. He is a regular reviewer for IEEE T-PAMI, T-MI, Nature Communications, CVPR, MICCAI and ISBI. He also works in translational health technology research, especially on health data analytics and telehealth.

His research in the development of machine learning and computer vision focuses on unsupervised and self-supervised deep learning models for biomedical image analysis, to improve image segmentation, retrieval, quantification, and classification without relying on labelled data.

Dagan Feng received the M. Eng. degree in electrical engineering and computer science (EECS) from Shanghai Jiao Tong University (SJTU), China in 1982, and the M. Sc. degree in biocybernetics and the Ph. D. degree in computer science from the University of California, Los Angeles (UCLA), USA in 1985 and 1988, respectively, where he received the Crump Prize for Excellence in Medical Engineering. After briefly working as assistant professor at the University of California, USA, he joined the University of Sydney, Australia at the end of 1988, as lecturer, progressing onto professor in Department of Computer Science and head of School of Information Technologies. He is a professor emeritus at School of Computer Science, The University of Sydney, Australia, and the founding director of the Biomedical and Multimedia Information Technology (BMIT) Research Group. Prof. Feng has led more than 50 key research projects, published over 700 scholarly research papers, pioneered several new research directions, and made a number of landmark contributions in his field. More importantly, however, is that many of his research results have been translated into solutions to real-life problems and have made tremendous improvements to the quality of life for those concerned. He is a fellow of ACS, HKIE, IET, IEEE, and the Australian Academy of Technological Sciences and Engineering (ATSE). He has served as Chair of the International Federation of Automatic Control (IFAC) Technical Committee on Biological and Medical Systems, Special Area Editor/Associate Editor/Editorial Board Member for a dozen of core journals in his area, and Scientific Advisor for a number of prestigious organizations. He has been invited to give over 100 keynote presentations in 23 countries and regions, and has organized/chaired over 100 major international conferences and symposia. He has also been appointed as Honorary Research Consultant, Royal Prince Alfred Hospital in Sydney, Australia; Chair Professor of Information Technology, Hong Kong Polytechnic University, China; Advisory Professor, Shanghai Jiao Tong University, China; Guest Professor, Northwestern Polytechnic University, China, Northeastern University, China, and Tsinghua University, China.

His research interests include biomedical systems modelling, functional imaging, biomedical information technology, and multimedia computing seeks to address the major challenges in “big data science” and provide innovative solutions for stochastic data acquisition, compression, storage, management, modelling, fusion, visualization, and communication. Currently, Prof. Feng and his research collaborators are working on new ways of improving the early detection of diseases such as cancer and dementia.

Jinman Kim received the B. Sc. (Hons.) and Ph. D. degrees in computer science from The University of Sydney, Australia in 2001 and 2006, respectively. He is an associate professor of computer science and the founding director of the Biomedical Data Analysis and Visualization (BDAV) Laboratory at The University of Sydney, Australia. He also serves as an associate director of School of Computer Science’s Biomedical and Multimedia Information Technology (BMIT) Research Group. He coleads the “digital health imaging”, as part of the Faculty of Engineering’s Digital Science Initiative, with the vision and strategy to improve the use and accessibility of medical imaging via AI innovations. Since 2006, he has been a research associate with the university’s leading teaching hospital, the Royal Prince Alfred Hospital. From 2008 to 2012, he was an ARC postdoctoral research fellow, with one year leave from 2009 to 2010 to join the MIRA Lab Research Group, Switzerland, as a marie curie senior research fellow. Since 2013, he has been with School of Computer Science, The University of Sydney where he was a senior lecturer, and was promoted to associate professor in 2016. He continuously publishes in top venues in his field and has received multiple competitive grants and scientific recognition. He is actively involved in his research communities where he is the vice president of the Computer Graphics Society (CGS), A/Editor of Computer Methods and Program in Biomedicine (CMPB), A/Editor of The Visual Computer (TVCJ), and Reviewer for all major journals and conferences in his field. He has actively focused on research translation where he has worked closely with clinical partners to take his research into clinical practice. He is the research director of the Nepean Telehealth and Technology Centre (NTTC) at Nepean Hospital, NSW Health, responsible for translational telehealth and digital hospital research. Some of his research has been developed into clinical software that is being used at multiple hospitals. His work on telehealth has been recognized with multiple awards, including the 2016 Health Secretary Innovation Award from the NSW Ministry of Health.

His research interests include machine learning, biomedical image analysis and visualization, especially multimodal data processing, image-omics, and image data correlation to other health data.

Declarations of Conflict of interest

The authors declared that they have no conflicts of interest to this work.

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Wang, WC., Ahn, E., Feng, D. et al. A Review of Predictive and Contrastive Self-supervised Learning for Medical Images. Mach. Intell. Res. 20, 483–513 (2023). https://doi.org/10.1007/s11633-022-1406-4

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  • Received: 30 August 2022

  • Accepted: 13 December 2022

  • Published: 03 June 2023

  • Issue Date: August 2023

  • DOI: https://doi.org/10.1007/s11633-022-1406-4

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Keywords

  • Self-supervised learning (SSL)
  • contrastive learning
  • deep learning
  • medical image analysis
  • computer vision
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