MoCo-Transfer: Investigating out-of-distribution contrastive learning
  for limited-data domains
Yuwen Chen Helen Zhou Zachary C. Lipton
Medical imaging data is often siloed within hospitals, limiting the amount of
data 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 contrastive
representations from moment contrast (MoCo) pretraining on out-of-distribution
data to settings with limited data. We consider two X-ray datasets which image
different parts of the body, and compare transferring from each other to
transferring from ImageNet. We find that depending on quantity of labeled and
unlabeled data, contrastive pretraining on larger out-of-distribution datasets
can perform nearly as well or better than MoCo pretraining in-domain, and
pretraining on related domains leads to higher performance than if one were to
use the ImageNet pretrained weights. Finally, we provide a preliminary way of
quantifying similarity between datasets.