Self-supervised Learning For Medical Image Classification: A Systematic Review And Implementation Guidelines

Self-supervised learning has the potential to make significant contributions to the development of robust medical imaging models, potentially improving healthcare and patient outcomes. This review provides a systematic review of papers published between 2012 and 2022 on PubMed, Scopus, and ArXiv that applied self-supervised learning to medical imaging classification. A total of 412 relevant studies were screened and 79 papers were included for data extraction and analysis. This comprehensive effort synthesizes the collective knowledge of prior work and provides implementation guidelines for future researchers interested in applying self-supervised learning to medical imaging classification models.