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.
@article{arxiv.2007.06199,
title = {CheXphoto: 10,000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness},
author = {Nick A. Phillips and Pranav Rajpurkar and Mark Sabini and Rayan Krishnan and Sharon Zhou and Anuj Pareek and Nguyet Minh Phu and Chris Wang and Mudit Jain and Nguyen Duong Du and Steven QH Truong and Andrew Y. Ng and Matthew P. Lungren},
journal= {arXiv preprint arXiv:2007.06199},
year = {2020}
}