69 citations · 217 across the 36 of their papers we have counts for
5 papers · 1 filter
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…
Is Visual in-Context Learning for Compositional Medical Tasks within Reach?
Simon Reiß, Zdravko Marinov, Alexander Jaus +4
In this paper, we explore the potential of visual in-context learning to enable a single model to handle multiple tasks and adapt to new tasks during test time without re-training.…
Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets
Alexander Jaus, Zdravko Marinov, Constantin Seibold +4
Manually refining radiological segmentation masks is highly resource-intensive. To determine when this expert commitment is truly justified for the training of segmentation models,…
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…