  Clinical deployment of deep learning algorithms for chest x-ray
interpretation requires a solution that can integrate into the vast spectrum of
clinical workflows across the world. An appealing approach to scaled deployment
is to leverage the ubiquity of smartphones by capturing photos of x-rays to
share with clinicians using messaging services like WhatsApp. However, the
application of chest x-ray algorithms to photos of chest x-rays requires
reliable classification in the presence of artifacts not typically encountered
in digital x-rays used to train machine learning models. We introduce
CheXphoto, a dataset of smartphone photos and synthetic photographic
transformations of chest x-rays sampled from the CheXpert dataset. To generate
CheXphoto we (1) automatically and manually captured photos of digital x-rays
under different settings, and (2) generated synthetic transformations of
digital x-rays targeted to make them look like photos of digital x-rays and
x-ray films. We release this dataset as a resource for testing and improving
the robustness of deep learning algorithms for automated chest x-ray
interpretation on smartphone photos of chest x-rays.
