Self-supervised learning for medical image classification: a systematic review and implementation guidelines.

ABSTRACT: Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes. However, the prevailing paradigm of training deep learning models requires large quantities of labeled training data, which is both time-consuming and cost-prohibitive to curate for medical images. Self-supervised learning has the potential to make significant contributions to the development of robust medical imaging models through its ability to learn useful insights from copious medical datasets without labels. In this review, we provide consistent descriptions of different self-supervised learning strategies and compose a systematic review of papers published between 2012 and 2022 on PubMed, Scopus, and ArXiv that applied self-

SUBMITTER: Huang SC 

PROVIDER: S-EPMC10131505 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature