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

arXiv: https://arxiv.org/abs/1901.11210 · HTML: https://mlsanity.com/paper/1901.11210/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2019.

## Built on by (most cited first, 12 of 12)

- [TorchXRayVision: A library of chest X-ray datasets and models](https://mlsanity.com/paper/2111.00595/) (2021)
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- [RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice](https://mlsanity.com/paper/2111.08165/) (2021)
- [A Novel Interaction-based Methodology Towards Explainable AI with Better Understanding of Pneumonia Chest X-ray Images](https://mlsanity.com/paper/2104.12672/) (2021)
- [Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System](https://mlsanity.com/paper/2109.12453/) (2021)
- [Impact of class imbalance on chest x-ray classifiers: towards better evaluation practices for discrimination and calibration performance](https://mlsanity.com/paper/2112.12843/) (2021)
- [INSIGHT: Explainable Weakly-Supervised Medical Image Analysis](https://mlsanity.com/paper/2412.02012/) (2024)
- [Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images](https://mlsanity.com/paper/2102.07975/) (2021)
- [An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray Images](https://mlsanity.com/paper/2106.06911/) (2021)
- [Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare](https://mlsanity.com/paper/2204.13194/) (2022)
- [On the notion of Hallucinations from the lens of Bias and Validity in Synthetic CXR Images](https://mlsanity.com/paper/2312.06979/) (2023)

## Abstract (arXiv)

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