  Contrastive learning is a form of self-supervision that can leverage
unlabeled data to produce pretrained models. While contrastive learning has
demonstrated promising results on natural image classification tasks, its
application to medical imaging tasks like chest X-ray interpretation has been
limited. In this work, we propose MoCo-CXR, which is an adaptation of the
contrastive learning method Momentum Contrast (MoCo), to produce models with
better representations and initializations for the detection of pathologies in
chest X-rays. In detecting pleural effusion, we find that linear models trained
on MoCo-CXR-pretrained representations outperform those without
MoCo-CXR-pretrained representations, indicating that MoCo-CXR-pretrained
representations are of higher-quality. End-to-end fine-tuning experiments
reveal that a model initialized via MoCo-CXR-pretraining outperforms its
non-MoCo-CXR-pretrained counterpart. We find that MoCo-CXR-pretraining provides
the most benefit with limited labeled training data. Finally, we demonstrate
similar results on a target Tuberculosis dataset unseen during pretraining,
indicating that MoCo-CXR-pretraining endows models with representations and
transferability that can be applied across chest X-ray datasets and tasks.
