Dual-View Thyroid Ultrasound Classification via Dual Knowledge Distillation

Thyroid ultrasound diagnosis in clinical practice typically relies on both transverse and longitudinal views of the same lesion. However, most existing deep learning methods process these views independently or only perform simple feature fusion, which limits their ability to model cross-view semantic consistency and reduces diagnostic robustness. We propose an uncertainty-weighted mixture-of-experts (UMoE) framework with dual knowledge distillation for dual-view thyroid ultrasound classification.

The proposed model is built on a shared Vision Transformer backbone that encodes both dual-view and single-view branches. Cross-view knowledge distillation is performed on uncertainty-refined patch tokens to align lesion-related representations across views, while cross-level knowledge distillation transfers holistic semantic knowledge from the dual-view branch to the single-view branches through the CLS token.

Experiments on the in-house DTN5K dataset and an external public thyroid ultrasound dataset show that the proposed framework consistently outperforms recent single-view and dual-view baselines in accuracy, F1-score, and area under the receiver operating characteristic curve. Ablation studies further verify the effectiveness of the proposed dual-view training strategy and each major component.

The proposed UMoE framework improves cross-view consistency during training and enhances the predictive ability of each single-view branch, leading to more accurate and robust thyroid ultrasound classification.

This study provides a clinically relevant dual-view learning framework for thyroid ultrasound analysis and offers a practical strategy for improving the reliability of computer-aided thyroid nodule diagnosis.