  Recent advances in deep learning have led to a promising performance in many
medical image analysis tasks. As the most commonly performed radiological exam,
chest radiographs are a particularly important modality for which a variety of
applications have been researched. The release of multiple, large, publicly
available chest X-ray datasets in recent years has encouraged research interest
and boosted the number of publications. In this paper, we review all studies
using deep learning on chest radiographs, categorizing works by task:
image-level prediction (classification and regression), segmentation,
localization, image generation and domain adaptation. Commercially available
applications are detailed, and a comprehensive discussion of the current state
of the art and potential future directions are provided.
