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S. Nebelung

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Review Open access Sep 2026

Preoperative functional liver imaging score in hepatocellular carcinoma resection: systematic review and meta-analysis

To synthesize the evidence on the association between the preoperative functional liver imaging score (FLIS) and post-hepatectomy liver failure (PHLF), overall survival (OS), and the discriminative performance of FLIS for PHLF prediction, in patients undergoing hepatectomy for hepatocellular carcinoma (HCC). PubMed, Em...

Felix Barajas Ordonez, I. E. Martínez Safar, Simon D. Westfechtel et al. · 0 citations

Differential privacy representation geometry for medical image analysis

Differential privacy (DP)'s effect in medical imaging is typically evaluated only through end-to-end performance, leaving the mechanism of privacy-induced utility loss unclear. We introduce Differential Privacy Representation Geometry for Medical Imaging (DP-RGMI), a framework that interprets DP as a structured transfo...

S. T. Arasteh, Marziyeh Mohammadi, S. Nebelung et al. · 0 citations
#computer vision Jul 2026

Self-supervision drives representational convergence in medical foundation models more than clinical supervision

Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, what produces it, and whether it is clinically usable are untested, and the similar...

Soroosh Tayebi Arasteh, S. Ziegelmayer, Mahshad Lotfinia et al. · 0 citations
Jul 2026

Bayesian uncertainty estimation improves clinical decision making in medical AI agents

It is shown that Monte Carlo dropout, applied to a multi-task chest-radiograph classifier, provides an epistemic uncertainty signal that tracks generalisation across training-set scales and flags confident yet error-prone predictions.

Frederik Hauke, P. Wienholt, Christiane Kuhl et al. · 0 citations
Jul 2026

Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans

It is demonstrated that pairing a genomic language model with widely available clinical imaging can serve as a hypothesis-free discovery tool for gene--imaging associations invisible to conventional approaches.

Frederik Hauke, Jeremias Krause, P. Wienholt et al. · 0 citations

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