医学影像分类中，模型通过不确定度路由提升可靠性与透明性。
MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification
- 用狄利克雷分布量化每个图像块的不确定性，驱动特征路由。
- 在四种模态上将预期校准误差降低35%，提升决策可信度。
- 适合需要可解释性与安全性的临床医疗AI部署场景。
为确保临床安全应用，深度学习模型不仅需高精度，还需可靠的不确定性量化。现有医学视觉变压器常出现过度自信预测且缺乏透明性，尤其在噪声大、不平衡的临床数据下问题更显著。本文改进了基于原型学习和不确定性引导路由的医学变压器（MedFormer），引入狄利克雷分布对每个图像块进行证据型不确定性建模，实现实时不确定性量化与定位。该不确定性不仅是输出，更参与训练过程，过滤不可靠特征更新。同时，类别特定原型保证嵌入空间结构化，支持基于视觉相似性的决策。在乳腺钼靶、超声、MRI及组织病理学四种模态上的测试表明，该方法显著提升模型校准性，预期校准误差（ECE）最高降低35%，并改善选择性预测能力，即使准确率提升有限。
原文摘要 · Abstract (English)
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.
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