[2603.28315] Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.28315 (cs) [Submitted on 30 Mar 2026] Title:Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification Authors:Yangmei Chen, Zhongyuan Zhang, Xikun Zhang, Xinyu Hao, Mingliang Hou, Renqiang Luo, Ziqi Xu View a PDF of the paper titled Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification, by Yangmei Chen and 6 other authors View PDF HTML (experimental) Abstract: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 ...