32 papers
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…
Self-supervision drives representational convergence in medical foundation models more than clinical supervision
Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia +4
Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations on…
CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders
Soroosh Tayebi Arasteh, Sven Nebelung, Daniel Truhn
Frozen encoders are chosen by how well a lightweight head reads a finding from their features, not whether the geometry separates it. Nearest-neighbor discordance does, but with un…
Vision-language models for chest radiography do not always need the image
Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams +3
Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image. That inference is unsafe: a model exploit…
Can Agents Distinguish Visually Hard-to-Separate Diseases in a Zero-Shot Setting? A Pilot Study
Zihao Zhao, Frederik Hauke, Juliana De Castilhos +2
The rapid progress of multimodal large language models (MLLMs) has led to increasing interest in agent-based systems. While most prior work in medical imaging concentrates on autom…