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Self-supervised learning (SSL) has achieved remarkable performance in variousmedical imaging tasks by dint of priors from massive unlabelled data. However,regarding a specific downstream task, there is still a lack of an instructionbook on how to select suitable pretext tasks and implementation detailsthroughout the standard ``pretrain-then-finetune'' workflow. In this work, wefocus on exploiting the capacity of SSL in terms of four realistic andsignificant issues: (1) the impact of SSL on imbalanced datasets, (2) thenetwork architecture, (3) the applicability of upstream tasks to downstreamtasks and (4) the stacking effect of SSL and common policies for deep learning.We provide a large-scale, in-depth and fine-grained study through extensiveexperiments on predictive, contrastive, generative and multi-SSL algorithms.Based on the results, we have uncovered several insights. Positively, SSLadvances class-imbalanced learning mainly by boosting the performance of therare class, which is of interest to clinical diagnosis. Unfortunately, SSLoffers marginal or even negative returns in some cases, including severelyimbalanced and relatively balanced data regimes, as well as combinations withcommon training policies. Our intriguing findings provide practical guidelinesfor the usage of SSL in the medical context and highlight the need fordeveloping universal pretext tasks to accommodate diverse applicationscenarios.

Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and Tasks