MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification

arXiv:2604.08868v1 Announce Type: cross Abstract: To ensure safe clinical integration, deep learning models must provide more than just high accuracy; they require dependable uncertainty quantification. While current Medical Vision Transformers perform well, they frequently struggle with overconfident predictions and a lack of transparency, issues that are magnified by the noisy and imbalanced nature of clinical data. To address this, we enhanced the modified Medical Transformer (MedFormer) that