Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Applied Self-Supervised Learning: Review of the State-of-the-Art and Implementations in Medicine

Version 1 : Received: 9 August 2021 / Approved: 11 August 2021 / Online: 11 August 2021 (08:27:57 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Informatics 2021, 8, 59
DOI: 10.3390/informatics8030059

Abstract

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently in 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 has the ability to 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 2014-2020. 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.

Keywords

self-supervised learning; medicine; healthcare; representation learning; unlabeled data

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