A Dual-Branch Deep Learning Framework with Mask-Guided Attention for Thyroid Nodule Classification in Ultrasound Images

Xueping Liu¹Jiajun Zhou²Chuang Xu³Zuojun Fu¹Yuwang Zhou¹Lulu Jiang¹Tianshu Xie^1,2Lei Wu⁴Yun Fang^1*Meiyi Yang^2*

• ¹People's Hospital of Quzhou, Quzhou, China
• ²Yangtze River Delta Research Institute, University of Electronic Science and Technology of China, Quzhou, China
• ³Wenzhou Medical University, Wenzhou, China
• ⁴University of Electronic Science and Technology of China School of Electronic Science and Engineering, Chengdu, China

Thyroid nodules are common, and accurate classification into benign or malignant types is essential for effective clinical management. Although high-resolution ultrasound is the primary diagnostic tool, its accuracy is limited by operator dependency. Recent advances in deep learning have shown promise for automated and objective assessment, but many existing methods lack focus on lesion-specific regions, compromising model robustness. To overcome these limitations, we propose a novel dual-branch deep learning framework that combines lesion segmentation and classification. A key feature of this framework is a nodule mask–guided feature enhancement module, which leverages probability masks from the segmentation branch to guide the classification branch toward diagnostically relevant regions while suppressing irrelevant information. Evaluated on ultrasound datasets from three medical centers, our approach demonstrates superior classification accuracy compared to baseline methods, highlighting its potential as a reliable computer-aided diagnosis tool for thyroid nodules.