  Over the last decade, supervised deep learning on manually annotated big data
has been progressing significantly on computer vision tasks. But the
application of deep learning in medical image analysis was limited by the
scarcity of high-quality annotated medical imaging data. An emerging solution
is self-supervised learning (SSL), among which contrastive SSL is the most
successful approach to rivalling or outperforming supervised learning. This
review investigates several state-of-the-art contrastive SSL algorithms
originally on natural images as well as their adaptations for medical images,
and concludes by discussing recent advances, current limitations, and future
directions in applying contrastive SSL in the medical domain.
