most citedPerformance uncertainty in medical image analysis: a large-scale investigation of confidence intervals

2 citations · 6 across the 8 of their papers we have counts for

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cs.CV2026

HeiCo-FOCUS: A Clinically Grounded Dataset for Long-Context Video Understanding

Leon Mayer, Lucas Luttner, Patrick Godau +42

Recent advances in Vision-Language Models (VLMs) have led to rapid progress in video understanding across a wide range of benchmark tasks. However, existing evaluations largely foc…

cs.CV2026

nnFoundation: 3D Foundation Models for Radiology

Constantin Ulrich Harsy, Tassilo Wald, Karol Gotkowski +80

Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise…

cs.CV2026

Towards Global AI-Driven Cervical Cancer Screening

Thuy Nuong Tran, Ömer Sümer, Evangelia Christodoulou +15

The global elimination of cervical cancer is a key public health goal set by the World Health Organization (WHO), with screening programs reducing mortality by up to 80%. However,…

cs.CV2026★ 2 cited

Performance uncertainty in medical image analysis: a large-scale investigation of confidence intervals

Pascaline André, Charles Heitz, Evangelia Christodoulou +10

Performance uncertainty quantification is essential for reliable validation and eventual clinical translation of medical imaging artificial intelligence (AI). Confidence intervals…

cs.CV2025★ 1 cited

Medical Imaging AI Competitions Lack Fairness

Annika Reinke, Evangelia Christodoulou, Sthuthi Sadananda +34

Benchmarking competitions are central to the development of artificial intelligence (AI) in medical imaging, defining performance standards and shaping methodological progress. How…

cs.CV2025

6 Fingers, 1 Kidney: Natural Adversarial Medical Images Reveal Critical Weaknesses of Vision-Language Models

Leon Mayer, Piotr Kalinowski, Caroline Ebersbach +6

Vision-language models (VLMs) are increasingly integrated into clinical workflows. However, existing benchmarks primarily assess performance on common anatomical presentations and…

cs.CV2025

Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study

Leon Mayer, Tim Rädsch, Dominik Michael +8

While traditional computer vision models have historically struggled to generalize to endoscopic domains, the emergence of foundation models has shown promising cross-domain perfor…

cs.CV2025★ 2 cited

False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims

Evangelia Christodoulou, Annika Reinke, Pascaline Andrè +23

Performance comparisons are fundamental in medical imaging Artificial Intelligence (AI) research, often driving claims of superiority based on relative improvements in common perfo…