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Self-supervised learning has enabled significant improvements on naturalimage benchmarks. However, there is less work in the medical imaging domain inthis area. The optimal models have not yet been determined among the variousoptions. Moreover, little work has evaluated the current applicability limitsof novel self-supervised methods. In this paper, we evaluate a range of currentcontrastive self-supervised methods on out-of-distribution generalization inorder to evaluate their applicability to medical imaging. We show thatself-supervised models are not as robust as expected based on their results innatural imaging benchmarks and can be outperformed by supervised learning withdropout. We also show that this behavior can be countered with extensiveaugmentation. Our results highlight the need for out-of-distributiongeneralization standards and benchmarks to adopt the self-supervised methods inthe medical imaging community.

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data