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Medical imaging data is often siloed within hospitals, limiting the amount ofdata available for specialized model development. With limited in-domain data,one might hope to leverage larger datasets from related domains. In this paper,we analyze the benefit of transferring self-supervised contrastiverepresentations from moment contrast (MoCo) pretraining on out-of-distributiondata to settings with limited data. We consider two X-ray datasets which imagedifferent parts of the body, and compare transferring from each other totransferring from ImageNet. We find that depending on quantity of labeled andunlabeled data, contrastive pretraining on larger out-of-distribution datasetscan perform nearly as well or better than MoCo pretraining in-domain, andpretraining on related domains leads to higher performance than if one were touse the ImageNet pretrained weights. Finally, we provide a preliminary way ofquantifying similarity between datasets.

MoCo-Transfer: Investigating out-of-distribution contrastive learning for limited-data domains