[2604.08868] MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2604.08868 (eess) [Submitted on 10 Apr 2026] Title:MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification Authors:Mohammed Maaz Sibhai, Abedalrhman Alkhateeb, Saad B. Ahmed View a PDF of the paper titled MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification, by Mohammed Maaz Sibhai and 2 other authors View PDF HTML (experimental) 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 incorporates prototype-based learning and uncertainty-guided routing, by utilizing a Dirichlet distribution for per-token evidential uncertainty, our framework can quantify and localize ambiguity in real-time. This uncertainty is not just an output but an active participant in the training process, filtering out unreliable feature updates. Furthermore, the use of class-specific prototypes ensures the embedding space remains structured, allowing for decisions based on visual similarity. Testing across four modalities (mammography, ultrasound, MRI, and histopathology) confirm...