Large vision models for robust and trustworthy medical image understanding
This collaboration investigates the applicability of large vision models to medical imaging, addressing robustness, generalization, and trustworthiness under domain shift and limited supervision.
Abstract
This collaboration investigates the applicability of large vision models to medical imaging, addressing robustness, generalization, and trustworthiness under domain shift and limited supervision. The work examined the behavior of foundation-scale models transferred from natural-image pretraining to medical imaging tasks. Methodological contributions include empirical analyses of cross-domain generalization, sensitivity to spurious correlations, and limitations of naïve scaling, alongside proposed evaluation protocols and architectural adaptations to improve robustness and reliability in medical contexts.
Perspective
The collaboration supported doctoral mentorship and strengthened national expertise at the intersection of foundation models and medical imaging. The insights gained inform ongoing work on principled evaluation and responsible deployment of large vision models in medical applications.
