12 papers
Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets
Alexander Jaus, Zdravko Marinov, Constantin Seibold +4
Manually refining radiological segmentation masks is highly resource-intensive. To determine when this expert commitment is truly justified for the training of segmentation models,…
The Data Manifold under the Microscope
Marios Koulakis, Constantin Seibold
A significant gap exists between theory and practice in deep learning. Generalization and approximation error bounds are often derived for simplified models or are too loose to be…
The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
Jakob Dexl, Katharina Jeblick, Andreas Mittermeier +27
We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body PET/CT under a compositional generaliza…
Region-Normalized DPO for Medical Image Segmentation under Noisy Judges
Hamza Kalisch, Constantin Seibold, Jens Kleesiek +2
While dense pixel-wise annotations remain the gold standard for medical image segmentation, they are costly to obtain and limit scalability. In contrast, many deployed systems alre…
Does Biomedical Training Lead to Better Medical Performance?
Amin Dada, Marie Bauer, Amanda Butler Contreras +4
Large Language Models (LLMs) are expected to significantly contribute to patient care, diagnostics, and administrative processes. Emerging biomedical LLMs aim to address healthcare…
CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation
Hamza Kalisch, Fabian Hörst, Jens Kleesiek +2
As medical imaging is central to diagnostic processes, automating the generation of radiology reports has become increasingly relevant to assist radiologists with their heavy workl…