215 citations
- Heidelberg UniversityDE14 papers
- National Center for Tumor DiseasesDE14 papers
- University Hospital HeidelbergDE14 papers
- University Hospital Carl Gustav CarusDE11 papers
- Fresenius (Germany)DE9 papers
- RWTH Aachen UniversityDE9 papers
- Universitätsklinikum AachenDE5 papers
- German Cancer Research CenterDE4 papers
- Deutsches Herzzentrum MünchenDE3 papers
- Stanford UniversityUS3 papers
- Technical University of MunichDE3 papers
- Technische Universität DresdenDE3 papers
19 papers
Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening
Anna Hinterberger, Jonas Bohn, Dasha Trofimova +13
Non-invasive colorectal cancer (CRC) screening represents a key opportunity to improve colonoscopy participation rates and reduce CRC mortality. This study explores the potential o…
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…
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
Max Kirchner, Hanna Hoffmann, Alexander C. Jenke +16
Developing generalizable surgical AI requires multi-institutional data, yet privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate. It…
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…
Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints
Waldemar Hahn, Jan-Niklas Eckardt, Christoph Röllig +3
The generation of synthetic clinical trial data offers a promising approach to mitigating privacy concerns and data accessibility limitations in medical research. However, ensuring…