Author

J. Müller

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Preprint Sep 2026

A Principled Approach to Unsupervised Anomaly Detection

Traditional unsupervised anomaly detection (UAD) methods are designed to flag or localise deviations from a normative distribution, ignoring the underlying generative mechanisms of the anomalies. Yet the nature of an anomaly is often as important as its presence. We reformulate UAD as a Bayesian inverse problem, in whi...

James Myles, Matthew Baugh, J. Müller et al. · 2 citations

Jul 2026

Panda: Unsupervised Pelvic Anomaly Detection for Real-Time MR Imaging

A Dinomaly-based unsupervised anomaly detection framework adapted for pelvic MRI that learns normative representations from healthy cases and flags deviations without requiring labels is presented.

Anika Knupfer, M. Lindholz, J. Müller et al. · arXiv.org · 0 citations