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

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…

• NI
  National Institutes of Health

  Awards: AR077604, R01 AR077604, AR079431, P41 EB027060, 75N92020C00021, R01 AR079431, R01 EB002524, EB002524, R01 LM012966, 75N92020C00008