Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System
arXiv:1901.11210
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
arXiv commentSubmitted to MIDL2020
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- Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
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- TorchXRayVision: A library of chest X-ray datasets and models
- Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays
- ViDi: Descriptive Visual Data Clustering as Radiologist Assistant in COVID-19 Streamline Diagnostic
- Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR images
- An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder
- Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System
- Diagnosis of COVID-19 and Non-COVID-19 Patients by Classifying Only a Single Cough Sound
- Twin Augmented Architectures for Robust Classification of COVID-19 Chest X-Ray Images