Get More Out of Your Annotated Medical Images with Self-Supervised Learning

Data scarcity is a perennial problem when applying deep learning (DL) to medical imaging. In vision tasks related to natural images, DL practitioners often have access to astoundingly large annotated data sets on which they can train. However, due to privacy concerns and the expense of creating them, access to large annotated data sets is rare in medical imaging. The natural follow-up question is: How can practitioners in the field of medical imaging best use DL given limited data? In this article, I'll discuss one approach to stretch the use of available data, called self-supervised learning.