patched-Diffusion-Models-UAD and Conditioned-Diffusion-Models-UAD
About patched-Diffusion-Models-UAD
FinnBehrendt/patched-Diffusion-Models-UAD
Codebase for Patched Diffusion Models for Unsupervised Anomaly Detection .
This project helps radiologists and medical researchers automatically identify anomalies like tumors or lesions in brain MRI scans. It takes a collection of healthy brain MRI scans as input to learn what a 'normal' brain looks like. It then compares this reference to new MRI scans, highlighting any pixel-level deviations that could indicate a pathology. This tool is for clinicians or researchers working with brain imaging.
About Conditioned-Diffusion-Models-UAD
FinnBehrendt/Conditioned-Diffusion-Models-UAD
Codebase for Conditioned Diffusion Models for Unsupervised Anomaly Detection
This project helps medical professionals and researchers automatically identify abnormalities in brain MRI scans without needing pre-labeled examples of diseases. You input a brain MRI image, and the system reconstructs a 'healthy' version, highlighting differences that indicate potential anomalies. Radiologists, neurologists, and clinical researchers would use this to improve the precision of anomaly detection in diagnostic imaging.
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