caiyu6666/DDAD
[MICCAI 2022] Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
This project helps radiologists and medical researchers automatically detect abnormalities in chest X-ray images. It takes standard chest X-rays as input and identifies regions that deviate from normal patterns, indicating potential anomalies. This is designed for clinical researchers and practitioners working with medical imaging to improve diagnostic workflows.
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Use this if you are a medical imaging researcher or radiologist needing to efficiently identify anomalies in large datasets of chest X-rays, particularly for academic research purposes.
Not ideal if you need a certified medical device for direct patient diagnosis, as this tool is currently intended for academic research.
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Language
Python
License
MIT
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Last pushed
Aug 12, 2025
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