Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System
원문 초록
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
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FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
EviNet: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy Environments
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and Approximations
GEM: Gaussian Embedding Modeling for Out-of-Distribution Detection in GUI Agents
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Masking
토론
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