7 citations · 8 across the 10 of their papers we have counts for
13 papers · 1 filter
From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Tasks
Carlos Schmidt, Simon Reiß
Visual in-context learning models are designed to adapt to new tasks by leveraging a set of example input-output pairs, enabling rapid generalization without task-specific fine-tun…
Probing Intrinsic Medical Task Relationships: A Contrastive Learning Perspective
Jonas Muth, Zdravko Marinov, Simon Reiß
While much of the medical computer vision community has focused on advancing performance for specific tasks, the underlying relationships between tasks, i.e., how they relate, over…
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.…
Conquering the Retina: Bringing Visual in-Context Learning to OCT
Alessio Negrini, Simon Reiß
Recent advancements in medical image analysis have led to the development of highly specialized models tailored to specific clinical tasks. These models have demonstrated exception…
CHAOS: Chart Analysis with Outlier Samples
Omar Moured, Yufan Chen, Ruiping Liu +4
Charts play a critical role in data analysis and visualization, yet real-world applications often present charts with challenging or noisy features. However, "outlier charts" pose…
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…