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Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior
Unsupervised anomaly detection (UAD) aims to localize abnormal regions in medical scans without pixel-level annotations. A typical strategy seeks to reconstruct a pseudo-healthy image that preserves subject-specific anatomy. Recently, diffusion models have been proposed to perform UAD. However, thes
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