Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System

Joseph Paul Cohen, Paul Bertin, Vincent Frappier

Keywords: Chest X-Ray, Radiology, Deep Learning

TL;DR: Bridging the gap between Deep Learning researchers and medical professionals for Chest X-ray assistive systems.

Abstract: In order to bridge the gap between Deep Learning researchers and medical professionals we develop a very accessible free prototype system which can be used by medical professionals to understand the reality of Deep Learning tools for chest X-ray diagnostics. The system is designed to be a second opinion where a user can process an image to confirm or aid in their diagnosis. Code and network weights are 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. This paper discusses the three main components in detail: out-of-distribution detection, disease prediction, and prediction explanation. The system open source and freely available.

Track: full conference paper

Paper Type: both

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/chester-a-web-delivered-locally-computed/code)