most citednnInteractive: Redefining 3D Promptable Segmentation

12 citations · 12 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Feature Space Guidance for Breast Cancer Classification in DCE-MRI

Benjamin Hamm, Yannick Kirchhoff, Maximilian Rokuss +6

Dynamic contrast enhanced breast MRI (DCE-MRI) is a powerful clinical tool for breast cancer detection, providing high resolution anatomical detail together with rich temporal cont…

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.CV2026

Resolution Meets Reduction: Efficient Visual Context for 3D Radiology Report Generation

Jonathan Suprijadi, Raphael Stock, Moritz Langenberg +10

Vision-language models offer a promising path toward automating radiology report generation, but applying them to full 3D CT volumes poses substantial computational challenges. Mod…

cs.CV2025

VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation

Maximilian Rokuss, Moritz Langenberg, Yannick Kirchhoff +8

We introduce VoxTell, a vision-language model for text-prompted volumetric medical image segmentation. It maps free-form descriptions, from single words to full clinical sentences,…

cs.CV2025★ 12 cited

nnInteractive: Redefining 3D Promptable Segmentation

Fabian Isensee, Maximilian Rokuss, Lars Krämer +10

Accurate and efficient 3D segmentation is essential for both clinical and research applications. While foundation models like SAM have revolutionized interactive segmentation, thei…