5 papers
TAP-CT: 3D Task-Agnostic Pretraining of Computed Tomography Foundation Models
Tim Veenboer, George Yiasemis, Eric Marcus +4
Existing foundation models (FMs) in the medical domain often require extensive fine-tuning or rely on training resource-intensive decoders, while many existing encoders are pretrai…
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…
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…
Current Pathology Foundation Models are unrobust to Medical Center Differences
Edwin D. de Jong, Eric Marcus, Jonas Teuwen
Pathology Foundation Models (FMs) hold great promise for healthcare. Before they can be used in clinical practice, it is essential to ensure they are robust to variations between m…