UG-FPR: uncertainty-guided feature perturbation and refinement for medical foundation models
A layer-wise uncertainty-guided feature perturbation and refinement framework that operates directly in representation space and can be seamlessly integrated into pretrained transformer encoders is proposed, validating the effectiveness of uncertainty-guided representation diffusion for medical visual understanding.