Clinical deployment of deep learning algorithms for chest x-ray interpreta- tion requires a solution that can inte- grate into the vast spectrum of clini- cal workflows across the world.
An appealing approach to scaled deployment is to leverage the ubiquity of smart- phones 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 arti- facts not typically encountered in dig- ital x-rays used to train machine learn- ing models. We introduce CheXphoto, a dataset of smartphone photos and synthetic photographic transformations of chest x-rays sampled from the CheX- pert dataset. To generate CheXphoto we (1) automatically and manually captured photos of digital x-rays under dif- ferent settings, and (2) generated synthetic transformations of digital x-rays targeted to make them look like pho- tos of digital x-rays and x-ray films.
We release this dataset as a resource for testing and improving the robust- ness of deep learning algorithms for au- tomated chest x-ray interpretation on smartphone photos of chest x-rays.
