activity
20232026
most citedSAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model

65 citations · 79 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scans

Benjamin Hamm, Nico Albert Disch, Maximilian Rokuss +3

Dynamic contrast-enhanced breast MRI (DCE-MRI) is rich in anatomical and perfusion information, but its reliance on gadolinium-based contrast agents raises safety concerns and adds…

cs.CV2026

nnFoundation: 3D Foundation Models for Radiology

Constantin Ulrich Harsy, Tassilo Wald, Karol Gotkowski +80

Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise…

cs.CV2025

CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series

Nico Albert Disch, Saikat Roy, Constantin Ulrich +5

Forecasting how 3D medical scans evolve over time is important for disease progression, treatment planning, and developmental assessment. Yet existing models either rely on a singl…

cs.CV2025

Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging

Nico Albert Disch, Yannick Kirchhoff, Robin Peretzke +5

Understanding temporal dynamics in medical imaging is crucial for applications such as disease progression modeling, treatment planning and anatomical development tracking. However…

cs.CV2023★ 14 cited

The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography

Kaiyuan Yang, Fabio Musio, Yihui Ma +112

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to influence the risk, severity, an…

eess.IV2023★ 65 cited

SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model

Saikat Roy, Tassilo Wald, Gregor Koehler +5

Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting. With the recent introduction of the Segment Anything…