7 citations · 8 across the 5 of their papers we have counts for
3 papers · 2 filters
From Explainable to Explained AI: Ideas for Falsifying and Quantifying Explanations
Yoni Schirris, Eric Marcus, Jonas Teuwen +2
Explaining deep learning models is essential for clinical integration of medical image analysis systems. A good explanation highlights if a model depends on spurious features that…
Towards Robust Foundation Models for Digital Pathology
Jonah Kömen, Edwin D. de Jong, Julius Hense +9
Biomedical Foundation Models (FMs) are rapidly transforming AI-enabled healthcare research and entering clinical validation. However, their susceptibility to learning non-biologica…
Foundation Models in Medical Imaging: A Review and Outlook
Vivien van Veldhuizen, Vanessa Botha, Chunyao Lu +10
Foundation models (FMs) are changing the way medical images are analyzed by learning from large collections of unlabeled data. Instead of relying on manually annotated examples, FM…