A Survey of the Impact of Self-Supervised Pretraining for Diagnostic
  Tasks with Radiological Images
Blake VanBerlo Jesse Hoey Alexander Wong
Self-supervised pretraining has been observed to be effective at improving
feature representations for transfer learning, leveraging large amounts of
unlabelled data. This review summarizes recent research into its usage in
X-ray, computed tomography, magnetic resonance, and ultrasound imaging,
concentrating on studies that compare self-supervised pretraining to fully
supervised learning for diagnostic tasks such as classification and
segmentation. The most pertinent finding is that self-supervised pretraining
generally improves downstream task performance compared to full supervision,
most prominently when unlabelled examples greatly outnumber labelled examples.
Based on the aggregate evidence, recommendations are provided for practitioners
considering using self-supervised learning. Motivated by limitations identified
in current research, directions and practices for future study are suggested,
such as integrating clinical knowledge with theoretically justified
self-supervised learning methods, evaluating on public datasets, growing the
modest body of evidence for ultrasound, and characterizing the impact of
self-supervised pretraining on generalization.