43 citations
- Technical University of MunichDE9 papers
- TUM KlinikumDE9 papers
- Heidelberg UniversityDE6 papers
- University Hospital HeidelbergDE6 papers
- Charité - Universitätsmedizin BerlinDE5 papers
- RWTH Aachen UniversityDE5 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE4 papers
- Radboud University Medical CenterNL4 papers
- Radboud University NijmegenNL4 papers
- Else Kröner Fresenius Center for Digital HealthDE3 papers
- German Centre for Cardiovascular ResearchDE3 papers
- Harvard UniversityUS3 papers
20 papers
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…
Deformable Image Registration for Self-supervised Cardiac Phase Detection in Multi-View Multi-Disease Cardiac Magnetic Resonance Images
Sven Koehler, Sarah Kaye Mueller, Jonathan Kiekenap +6
Cardiovascular magnetic resonance (CMR) is the gold standard for assessing cardiac function, but individual cardiac cycles complicate automatic temporal comparison or sub-phase ana…
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)…
Multi-step retrieval and reasoning improves radiology question answering with large language models
Sebastian Wind, Jeta Sopa, Daniel Truhn +9
Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-aug…
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…
Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms
Juan Miguel Lopez Alcaraz, Ebenezer Oloyede, David Taylor +2
Background: Electrocardiogram (ECG) analysis has emerged as a promising tool for detecting physiological changes linked to non-cardiac disorders. Given the close connection between…