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.
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Use this if you need to detect abnormalities in brain MRI scans without relying on large, labor-intensive datasets of annotated pathological images.
Not ideal if you require anomaly detection for other anatomical regions or if you prefer a method that doesn't use a patch-based approach, in which case the 'Conditioned Diffusion Models' project from the same authors might be more suitable.
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Mar 19, 2025
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