This repository contains the python code used to build and train the models used in our paper Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System.
The conda environment used for this project is provided with the spec-file.txt To use the spec file to create an identical environment, run :
conda create --name myenv --file spec-file.txt
The different model files are in the folder Models. You can refer to the file Models/INFO.md for details about how the different models were trained. To load them, use the torch modules defined in model.py.
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.