activity
20242026
collaborators

11 papers

cs.CV2026

Cross-Modal MRI Ovary Segmentation in Endometriosis Using Unpaired TVUS Prototype Priors

Xingjian Kang, Lina Felsner, Dominik Perrin +5

Transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) provide complementary information for endometriosis image analysis, yet existing studies mainly focus on single-…

cs.LG2026

The Mean is the Mirage: Entropy-Adaptive Model Merging under Heterogeneous Domain Shifts in Medical Imaging

Sameer Ambekar, Reza Nasirigerdeh, Peter J. Schuffler +3

Model merging under unseen test-time distribution shifts often renders naive strategies, such as mean averaging unreliable. This challenge is especially acute in medical imaging, w…

cs.LG2026

A Master Class on Reproducibility: A Student Hackathon on Advanced MRI Reconstruction Methods

Lina Felsner, Sevgi G. Kafali, Hannah Eichhorn +9

We report the design, protocol, and outcomes of a student reproducibility hackathon focused on replicating the results of three influential MRI reconstruction papers: (a) MoDL, an…

cs.LG2026

Uncertainty-guided Generation of Dark-field Radiographs

Lina Felsner, Henriette Bast, Tina Dorosti +4

X-ray dark-field radiography provides complementary diagnostic information to conventional attenuation imaging by visualizing microstructural tissue changes through small-angle sca…

eess.IV2025

TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks

Johannes Kiechle, Stefan M. Fischer, Daniel M. Lang +5

The sharp rise in medical tomography examinations has created a demand for automated systems that can reliably extract informative features for downstream tasks such as tumor chara…

cs.CV2025

Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation

Stefan M. Fischer, Johannes Kiechle, Laura Daza +6

In this work, we introduce Progressive Growing of Patch Size, an automatic curriculum learning approach for 3D medical image segmentation. Our approach progressively increases the…