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

TL;DR

Chester is a web-based, locally computed chest X-ray disease prediction system that provides accessible, privacy-preserving second opinions for medical professionals, integrating out-of-distribution detection, disease prediction, and explanation features.

Contribution

It introduces a novel web-delivered system enabling local processing of X-ray images, bridging deep learning research and clinical practice with an open-source tool.

Findings

System effectively detects out-of-distribution images.

Accurate disease prediction results demonstrated.

Provides interpretable explanations for predictions.

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 here: https://mlmed.org/tools/xray