Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification
This addresses the challenge of deploying reliable AI diagnostic tools in real-world clinical settings with heterogeneous ultrasound data, though it appears incremental as it builds on existing multi-view and prototype learning techniques.
The paper tackled the problem of limited robustness and generalization in deep learning methods for thyroid nodule ultrasound classification across different devices and environments, and the result was that their proposed PEMV-thyroid framework consistently outperformed state-of-the-art methods, particularly in cross-device and cross-domain scenarios, leading to improved diagnostic accuracy and generalization.
Thyroid nodule classification using ultrasound imaging is essential for early diagnosis and clinical decision-making; however, despite promising performance on in-distribution data, existing deep learning methods often exhibit limited robustness and generalisation when deployed across different ultrasound devices or clinical environments. This limitation is mainly attributed to the pronounced heterogeneity of thyroid ultrasound images, which can lead models to capture spurious correlations rather than reliable diagnostic cues. To address this challenge, we propose PEMV-thyroid, a Prototype-Enhanced Multi-View learning framework that accounts for data heterogeneity by learning complementary representations from multiple feature perspectives and refining decision boundaries through a prototype-based correction mechanism with mixed prototype information. By integrating multi-view representations with prototype-level guidance, the proposed approach enables more stable representation learning under heterogeneous imaging conditions. Extensive experiments on multiple thyroid ultrasound datasets demonstrate that PEMV-thyroid consistently outperforms state-of-the-art methods, particularly in cross-device and cross-domain evaluation scenarios, leading to improved diagnostic accuracy and generalisation performance in real-world clinical settings. The source code is available at https://github.com/chenyangmeii/Prototype-Enhanced-Multi-View-Learning.