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. · 1 citation

Jul 2026

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

Female pelvic diseases remain an under researched area characterized by often delayed diagnosis. While pelvic MRI offers superior soft-tissue contrast for diagnosis and image-guided procedures, real-time anomaly detection remains challenging due to physiological motion, tissue deformation, and instrument artifacts. Exi...

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