215 citations · 590 across the 42 of their papers we have counts for
44 papers
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…
Listening for Airway Stenosis: A Foundation Model-Based Method for Rapid and Accessible Detection
Jean Groeninger, Zihao Zhao, Juliana de Castilhos +2
Airway stenosis can cause severe respiratory complications, yet its detection often relies on specialized examinations and medical imaging. This study explores the potential of aco…
CANDOR: Chance-Calibrated Neighborhood Discordance in Frozen Encoders for Medical Imaging
Soroosh Tayebi Arasteh, Sven Nebelung, Daniel Truhn
In medical imaging, one frozen foundation encoder is often reused for many tasks. The encoder is usually chosen by the area under the receiver operating characteristic curve (AUROC…
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…
Cross-modal linkage risk in clinical vision-language models
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Sven Nebelung +1
Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-level image-report correspondence.…
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…
Safety and accuracy follow different scaling laws in clinical large language models
Sebastian Wind, Tri-Thien Nguyen, Jeta Sopa +9
Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation that higher accuracy implies…
Differential privacy representation geometry for medical image analysis
Soroosh Tayebi Arasteh, Marziyeh Mohammadi, Sven Nebelung +1
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…
Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision
Soroosh Tayebi Arasteh, Mehdi Joodaki, Mahshad Lotfinia +2
Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports the tar…
Agentic retrieval-augmented reasoning reshapes collective reliability under model variability in radiology question answering
Mina Farajiamiri, Jeta Sopa, Saba Afza +9
Agentic retrieval-augmented reasoning pipelines are increasingly used to structure how large language models (LLMs) incorporate external evidence in clinical decision support. Thes…
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…
The pretraining domain outweighs the training objective in setting the privacy-utility trade-off of differentially private medical image analysis
Soroosh Tayebi Arasteh, Mina Farajiamiri, Mahshad Lotfinia +7
Differential privacy limits how much each training image can influence a medical imaging model but lowers its diagnostic accuracy. Pretrained weights reduce this loss. It is unknow…
Effect of Reporting Mode and Clinical Experience on Radiologists' Gaze and Image Analysis Behavior in Chest Radiography
Mahta Khoobi, Marc Sebastian von der Stueck, Felix Barajas Ordonez +11
Structured reporting (SR) and artificial intelligence (AI) may transform how radiologists interact with imaging studies. This prospective study (July to December 2024) evaluated th…
Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch +4
Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image clas…
Hallucination Filtering in Radiology Vision-Language Models Using Discrete Semantic Entropy
Patrick Wienholt, Sophie Caselitz, Robert Siepmann +6
To determine whether using discrete semantic entropy (DSE) to reject questions likely to generate hallucinations can improve the accuracy of black-box vision-language models (VLMs)…
Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Soroosh Tayebi Arasteh, Mina Shaigan, Christiane Kuhl +3
Self-supervised learning (SSL) has improved visual representation learning, but its value in chest radiography remains uncertain. DINOv3 extends earlier SSL models through Gram-anc…
MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
Patrick Wienholt, Christiane Kuhl, Jakob Nikolas Kather +2
Deep neural networks excel in radiological image classification but frequently suffer from poor interpretability, limiting clinical acceptance. We present MedicalPatchNet, an inher…
T2 Radiomic Features Are More Sensitive Than Mean T2 for Cartilage Load Response: A Stress MRI Study
Teresa Lemainque, Philipp Schad, Simon Westfechtel +6
Objective: To assess response-to-loading in a human cadaveric knee joint model under different loading conditions before and after meniscectomy Design: In this prospective study, s…
Diagnostic Accuracy of Open-Source Vision-Language Models on Diverse Medical Imaging Tasks
Gustav Müller-Franzes, Debora Jutz, Jakob Nikolas Kather +3
This retrospective study evaluated five VLMs (Qwen2.5, Phi-4, Gemma3, Llama3.2, and Mistral3.1) using the MedFMC dataset. This dataset includes 22,349 images from 7,461 patients en…
Three-dimensional end-to-end deep learning for brain MRI analysis
Radhika Juglan, Marta Ligero, Zunamys I. Carrero +9
Deep learning (DL) methods are increasingly outperforming classical approaches in brain imaging, yet their generalizability across diverse imaging cohorts remains inadequately asse…
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
Gustav Müller-Franzes, Firas Khader, Robert Siepmann +4
MRI and CT are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. Wh…
RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno Bressem +7
Large language models (LLMs) often generate outdated or inaccurate information based on static training datasets. Retrieval-augmented generation (RAG) mitigates this by integrating…