4 papers
Weighting What Matters: Boosting Sample Efficiency in Medical Report Generation via Token Reweighting
Alexander Weers, Daniel Rueckert, Martin J. Menten
Training vision-language models (VLMs) for medical report generation is often hindered by the scarcity of high-quality annotated data. This work evaluates the use of a weighted los…
A Graph-Based Framework for Interpretable Whole Slide Image Analysis
Alexander Weers, Alexander H. Berger, Laurin Lux +3
The histopathological analysis of whole-slide images (WSIs) is fundamental to cancer diagnosis but is a time-consuming and expert-driven process. While deep learning methods show p…
Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation
Laurin Lux, Alexander H. Berger, Alexander Weers +4
Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological…
Pitfalls of topology-aware image segmentation
Alexander H. Berger, Laurin Lux, Alexander Weers +3
Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuro…