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Review

Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations

1
Department of Informatics & Analytics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
2
Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
3
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
4
Department of Biological Engineering, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Antony Bryant
Informatics 2021, 8(3), 59; https://doi.org/10.3390/informatics8030059
Received: 9 August 2021 / Revised: 3 September 2021 / Accepted: 8 September 2021 / Published: 10 September 2021
(This article belongs to the Special Issue Machine Learning in Healthcare)
Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently, in medicine and healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that can take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state of the art published in each of those subsets between the years of 2014 and 2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data. View Full-Text
Keywords: self-supervised learning; healthcare; representation learning; medicine; computer vision; pathology; machine learning self-supervised learning; healthcare; representation learning; medicine; computer vision; pathology; machine learning
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MDPI and ACS Style

Chowdhury, A.; Rosenthal, J.; Waring, J.; Umeton, R. Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations. Informatics 2021, 8, 59. https://doi.org/10.3390/informatics8030059

AMA Style

Chowdhury A, Rosenthal J, Waring J, Umeton R. Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations. Informatics. 2021; 8(3):59. https://doi.org/10.3390/informatics8030059

Chicago/Turabian Style

Chowdhury, Alexander, Jacob Rosenthal, Jonathan Waring, and Renato Umeton. 2021. "Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations" Informatics 8, no. 3: 59. https://doi.org/10.3390/informatics8030059

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