  Self-supervision has demonstrated to be an effective learning strategy when
training target tasks on small annotated data-sets. While current research
focuses on creating novel pretext tasks to learn meaningful and reusable
representations for the target task, these efforts obtain marginal performance
gains compared to fully-supervised learning. Meanwhile, little attention has
been given to study the robustness of networks trained in a self-supervised
manner. In this work, we demonstrate that networks trained via self-supervised
learning have superior robustness and generalizability compared to
fully-supervised learning in the context of medical imaging. Our experiments on
pneumonia detection in X-rays and multi-organ segmentation in CT yield
consistent results exposing the hidden benefits of self-supervision for
learning robust feature representations.
