activity
20212026
most citedReference-guided Pseudo-Label Generation for Medical Semantic Segmentation

69 citations · 146 across the 27 of their papers we have counts for

collaborators

29 papers

cs.CL2026

Case2Flow: Bridging Patient Cases and Guideline Flowcharts through Multimodal Retrieval

Jiale Wei, Yufan Chen, Alexander Jaus +5

Medical guidelines encode rich, evidence-based decision logic, yet the specific decision artifact a clinician needs is hard to locate within a guideline, let alone across guideline…

cs.CV2026

Quo Vadis, Visual In-Context Learning? A Unified Benchmark Across Domains and Tasks

Pradnya Halady, Jiale Wei, Zdravko Marinov +2

Visual in-context learning has been proposed as a pathway towards dynamic models that can generate predictions based on a provided context and thereby can adapt to new vision tasks…

cs.CV2026

Beyond Model Size: Probing the Gaps in Visual in-Context Learning by Training a Tiny Model

Sunil Khatri, Steven Landgraf, Markus Ulrich +1

Visual in-Context Learning (VICL) aims at making progress towards adaptive vision models, that can -- based on a few examples -- adapt to a new task at test-time. With the history…

cs.CV2026

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…

cs.CV2026

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…

eess.IV2025

Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement

Cedric Zöllner, Simon Reiß, Alexander Jaus +3

When preoperative planning for surgeries is conducted on the basis of medical images, artificial intelligence methods can support medical doctors during assessment. In this work, w…