MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification
This work addresses the need for dependable uncertainty quantification in deep learning models for safe clinical integration, though it is incremental as it builds on existing transformer frameworks.
The paper tackled the problem of overconfident predictions and lack of transparency in medical image classification by enhancing a transformer model with uncertainty quantification and prototype-based learning, resulting in up to a 35% reduction in expected calibration error and improved selective prediction across four medical modalities.
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) confirms that our approach significantly enhances model calibration, reducing expected calibration error (ECE) by up to 35%, and improves selective prediction, even when accuracy gains are modest.