1 citations · 5 across the 8 of their papers we have counts for
9 papers
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…
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…
RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding
Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu +15
Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared embedding space, s…
Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus
Johannes Moll, Jannik Lübberstedt, Christoph Nuernbergk +21
Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distributed across dozens to hundr…
Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
Johannes Moll, Markus Graf, Tristan Lemke +7
Vision-language models (VLMs) often produce chain-of-thought (CoT) explanations that sound plausible yet fail to reflect the underlying decision process, undermining trust in high-…
Sex-based Bias Inherent in the Dice Similarity Coefficient: A Model Independent Analysis for Multiple Anatomical Structures
Hartmut Häntze, Myrthe Buser, Alessa Hering +2
Overlap-based metrics such as the Dice Similarity Coefficient (DSC) penalize segmentation errors more heavily in smaller structures. As organ size differs by sex, this implies that…
Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs
Juraj Vladika, Annika Domres, Mai Nguyen +10
Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to their clinical adoption and reg…
Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework
Sarah de Boer, Hartmut Häntze, Kiran Vaidhya Venkadesh +9
Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an importa…
Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans
Hartmut Häntze, Lina Xu, Maximilian Rattunde +5
Computed tomography (CT) segmentation models often contain classes that are not currently supported by magnetic resonance imaging (MRI) segmentation models. In this study, we show…