  Chest radiograph (or Chest X-Ray, CXR) is a popular medical imaging modality
that is used by radiologists across the world to diagnose heart or lung
conditions. Over the last decade, Convolutional Neural Networks (CNN), have
seen success in identifying pathologies in CXR images. Typically, these CNNs
are pretrained on the standard ImageNet classification task, but this assumes
availability of large-scale annotated datasets. In this work, we analyze the
utility of pretraining on unlabeled ImageNet or Chest X-Ray (CXR) datasets
using various algorithms and in multiple settings. Some findings of our work
include: (i) supervised training with labeled ImageNet learns strong
representations that are hard to beat; (ii) self-supervised pretraining on
ImageNet (~1M images) shows performance similar to self-supervised pretraining
on a CXR dataset (~100K images); and (iii) the CNN trained on supervised
ImageNet can be trained further with self-supervised CXR images leading to
improvements, especially when the downstream dataset is on the order of a few
thousand images.
