Self-supervised learning has enabled significant improvements on natural image benchmarks. However, there is less work in the medical imaging domain in this area. The optimal models have not yet been determined among the various options. Moreover, little work has evaluated the current applicability limits of novel self-supervised methods. In this paper, we evaluate a range of current contrastive self-supervised methods on out-of-distribution generalization in order to evaluate their applicability to medical imaging. We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout. We also show that this behavior can be countered with extensive augmentation. Our results highlight the need for out-of-distribution generalization standards and benchmarks to adopt the self-supervised methods in the medical imaging community.

本文评估了一系列当前的对比自我监督方法在医学成像中的适用性，发现自我监督模型的鲁棒性不如预期，可以被具有丰富变化的数据和带丢失的监督学习超越，同时强调自我监督方法在医学成像社区需要针对性的基准规范和评估标准。

评估自监督泛化: 在分布外多模态MRI数据中尝试Cake