Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System
This addresses the gap between deep learning researchers and medical professionals by offering an accessible, privacy-preserving tool for chest X-ray diagnostics, though it is incremental as it builds on existing methods for prediction and explanation.
The researchers developed Chester, a web-based system that provides chest X-ray disease predictions locally on the user's device to serve as a second opinion for medical professionals, with all patient data kept on the machine and processing done locally.
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 here: https://mlmed.org/tools/xray