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Self-supervised pretraining has been observed to be effective at improvingfeature representations for transfer learning, leveraging large amounts ofunlabelled data. This review summarizes recent research into its usage inX-ray, computed tomography, magnetic resonance, and ultrasound imaging,concentrating on studies that compare self-supervised pretraining to fullysupervised learning for diagnostic tasks such as classification andsegmentation. The most pertinent finding is that self-supervised pretraininggenerally 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 practitionersconsidering using self-supervised learning. Motivated by limitations identifiedin current research, directions and practices for future study are suggested,such as integrating clinical knowledge with theoretically justifiedself-supervised learning methods, evaluating on public datasets, growing themodest body of evidence for ultrasound, and characterizing the impact ofself-supervised pretraining on generalization.

A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images