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20242026
most citedWhy context matters in VQA and Reasoning: Semantic interventions for VLM input modalities

1 citations · 1 across the 5 of their papers we have counts for

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cs.CV2026

Assessing Pancreatic Ductal Adenocarcinoma Vascular Invasion: the PDACVI Benchmark

M. Riera-Marín, O. K. Sikha, J. Rodríguez-Comas +23

Surgical resection remains the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC), and eligibility depends on accurate assessment of vascular invasion…

cs.CV2026

Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance

Vanessa Emanuela Guarino, Claudia Winklmayr, Jannik Franzen +7

Uncertainty Quantification (UQ) is crucial for ensuring the reliability of automated image segmentations in safety-critical domains like biomedical image analysis or autonomous dri…

cs.CV2026

Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging

Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6

Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming a…

cs.CV2025

nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation

Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl +6

Semantic segmentation is crucial for various biomedical applications, yet its reliance on large annotated datasets presents a bottleneck due to the high cost and specialized expert…

cs.CV20244 cited

Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics

Lukas Klein, Carsten T. Lüth, Udo Schlegel +3

Explainable AI (XAI) is a rapidly growing domain with a myriad of proposed methods as well as metrics aiming to evaluate their efficacy. However, current studies are often of limit…