2 citations · 5 across the 7 of their papers we have counts for
7 papers
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…
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…
Automatic Fine-grained Segmentation-assisted Report Generation
Frederic Jonske, Constantin Seibold, Osman Alperen Koras +6
Reliable end-to-end clinical report generation has been a longstanding goal of medical ML research. The end goal for this process is to alleviate radiologists' workloads and provid…
Foreign object segmentation in chest x-rays through anatomy-guided shape insertion
Constantin Seibold, Hamza Kalisch, Lukas Heine +2
In this paper, we tackle the challenge of instance segmentation for foreign objects in chest radiographs, commonly seen in postoperative follow-ups with stents, pacemakers, or inge…
Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT
Hamza Kalisch, Fabian Hörst, Ken Herrmann +2
Lesion segmentation in PET/CT imaging is essential for precise tumor characterization, which supports personalized treatment planning and enhances diagnostic precision in oncology.…
Exploring the Limits of Deep Image Clustering using Pretrained Models
Nikolas Adaloglou, Felix Michels, Hamza Kalisch +1
We present a general methodology that learns to classify images without labels by leveraging pretrained feature extractors. Our approach involves self-distillation training of clus…