25 citations · 107 across the 32 of their papers we have counts for
8 papers · 1 filter
Single-Pass Decision Scores Enable Rapid Classification with Adjustable Thresholds in Medical Multimodal Large Language Models
Patrick Wienholt, Frederik Hauke, Christiane Kuhl +3
Multimodal large language models (MLLMs) answer binary clinical questions with a yes or no token, leaving the decision threshold implicit and fixed. We derive a continuous score fr…
The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging
Fabian Drexel, Bernhard Kainz, Jakob N. Kather +4
Joint-embedding predictive architectures (JEPAs) learn by predicting teacher representations of masked regions from visible context. Point prediction summarizes the conditional tar…
VIPER: An Expert-Curated Benchmark for Vision-Language Models in Veterinary Pathology
Luca L. Weishaupt, Simone de Brot, Javier Asin +9
Pathology vision-language models are advancing rapidly, yet existing benchmarks remain focused on human tissue, particularly oncology, leaving non-human pathology largely unaddress…
Large language models improve physician accuracy but lead to false reliance
Tirtha Chanda, Christoph Wies, Franziska Schramm +10
Retrieval-augmented large language models (LLMs) promise source-linked clinical support, but their value depends on whether displayed evidence guides rather than distorts physician…
Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans
Frederik Hauke, Jeremias Krause, Patrick Wienholt +6
The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, cannot assess genes that are only rarely aff…
Bayesian uncertainty estimation improves clinical decision making in medical AI agents
Frederik Hauke, Patrick Wienholt, Christiane Kuhl +4
Machine learning models for medical image analysis typically lack a reliable measure of confidence, limiting their use in ambiguous or atypical cases. Here we show that Monte Carlo…
Medical foundation models converge less under label supervision
Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia +4
Diagnostic classifiers and imaging biomarkers are fitted on the embeddings of medical foundation models. These models are replaced as new versions appear. This practice assumes tha…
From Clinical Intent to Clinical Model: Autonomous Coding-Agents for Clinician-driven AI Development
Zihao Zhao, Frederik Hauke, Juliana De Castilhos +4
Developing AI models that are useful in clinical practice, requires efficient collaboration between clinicians and AI developers. This poses a practical challenge: clinicians must…