215 citations · 1k across the 75 of their papers we have counts for
25 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…
FRAME: Separating sampling variation from performance disparities in medical image analysis
Mahshad Lotfinia, Daniel Truhn, Andreas Maier +1
Fairness audits of medical imaging models commonly report the largest performance difference between demographic subgroups and treat any positive value as evidence of bias. Yet a p…
Auditable CT Phenotyping Through Report-derived Radiological Observations
Riga Wu, Walter Witschey, Yicheng Li +9
Medical image foundation models can predict clinical phenotypes from computed tomography (CT), but strong performance leaves open whether they read disease-specific findings or sho…
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…
Vision-language models for chest radiography do not always need the image
Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams +4
Vision-language models that answer questions about chest radiographs are evaluated by their accuracy on labels derived from radiology reports. High benchmark accuracy is often inte…
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…
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…
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…
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…
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…
Abnormality-Driven Representation Learning for Radiology Imaging
Marta Ligero, Tim Lenz, Georg Wölflein +3
To date, the most common approach for radiology deep learning pipelines is the use of end-to-end 3D networks based on models pre-trained on other tasks, followed by fine-tuning on…
Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization
Firas Khader, Omar S. M. El Nahhas, Tianyu Han +4
The Transformer model has been pivotal in advancing fields such as natural language processing, speech recognition, and computer vision. However, a critical limitation of this mode…
In-context learning enables multimodal large language models to classify cancer pathology images
Dyke Ferber, Georg Wölflein, Isabella C. Wiest +8
Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this proce…
An Ordinal Regression Framework for a Deep Learning Based Severity Assessment for Chest Radiographs
Patrick Wienholt, Alexander Hermans, Firas Khader +5
This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs. We propose a framework that divides the ordinal regres…
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology
Omar S. M. El Nahhas, Marko van Treeck, Georg Wölflein +9
Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computation…
Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
Georg Wölflein, Dyke Ferber, Asier R. Meneghetti +6
Weakly supervised whole slide image classification is a key task in computational pathology, which involves predicting a slide-level label from a set of image patches constituting…
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining
Tianyu Han, Laura Žigutytė, Luisa Huck +9
Detecting misleading patterns in automated diagnostic assistance systems, such as those powered by Artificial Intelligence, is critical to ensuring their reliability, particularly…
Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers
Firas Khader, Jakob Nikolas Kather, Tianyu Han +4
Whole-Slide Imaging allows for the capturing and digitization of high-resolution images of histological specimen. An automated analysis of such images using deep learning models is…
Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
Sophia J. Wagner, Daniel Reisenbüchler, Nicholas P. West +28
Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of micr…