Deep learning has shown promise to augment radiologists and improve the standard of care globally. Two main issues that complicate deploying these systems are patient privacy and scaling to the global population. To deploy a system at scale with minimal computational cost while preserving privacy we present a web delivered (but locally run) system for diagnosing chest X-Rays. Code is delivered via a URL to a web browser (including cell phones) but the patient data remains on the users machine and all processing occurs locally. The system is designed to be used as a reference where a user can process an image to confirm or aid in their diagnosis. The system contains three main components: out-of-distribution detection, disease prediction, and prediction explanation. The system open source and freely available here: this https URL
Joseph Paul Cohen, Paul Bertin, Vincent Frappier. “Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System”. Jan. 2019, http://arxiv.org/abs/1901.11210.
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