1 citations · 1 across the 11 of their papers we have counts for
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A Principled Approach to Unsupervised Anomaly Detection
James Myles, Matthew Baugh, Johanna P. Müller +2
Traditional unsupervised anomaly detection (UAD) methods are designed to flag or localise deviations from a normative distribution, ignoring the underlying generative mechanisms of…
Panda: Unsupervised Pelvic Anomaly Detection for Real-Time MR Imaging
Anika Knupfer, Maximilian Lindholz, Johanna Paula Müller +4
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-guide…
Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering
Luca Hagen, Johanna P. Müller, Weitong Zhang +2
Small vision-language models (2-8B) are well-suited for clinical deployment due to privacy constraints, limited connectivity, and low-latency requirements favouring on-device or on…
Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology
Franciskus Xaverius Erick, Johanna Paula Müller, Bernhard Kainz
Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology. However, fine-tuning billion…
Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imaging
Anika Knupfer, Johanna P. Müller, Jordina A. Verdera +9
Pelvic diseases in women of reproductive age represent a major global health burden, with diagnosis frequently delayed due to high anatomical variability, complicating MRI interpre…