AI RESEARCH
Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review
arXiv CS.CV
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ArXi:2510.14462v2 Announce Type: replace Unsupervised anomaly detection (UAD) based on deep generative modelling has been increasingly explored for identifying pathological brain abnormalities without requiring voxel-level annotations. By learning the distribution of healthy anatomy and generating pseudo-healthy reconstructions, these methods aim to localise deviations in a pathology-agnostic manner.