caiyu6666/DDAD-ASR
[MedIA 2023] Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
This project helps medical professionals like radiologists or diagnostic technicians identify abnormalities in medical images. It takes standard medical image scans (like X-rays or MRIs) as input and highlights regions that deviate from normal patterns, helping pinpoint potential issues. This tool is designed for those who review a large volume of medical images and need assistance in detecting subtle or rare anomalies.
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Use this if you are a medical researcher or practitioner working with medical imaging and need an automated method to highlight unusual findings.
Not ideal if you are looking for a diagnostic tool for clinical decision-making or a fully automated solution without human oversight.
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57
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Language
Python
License
MIT
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Last pushed
Aug 12, 2025
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