collaborators

7 papers

cs.CV2026

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.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,…

q-bio.OT2026

Current validation practice undermines surgical AI development

Annika Reinke, Ziying O. Li, Minu D. Tizabi +97

Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation as an important contr…

cs.CV2026

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

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

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…