The classification of carotid artery ultrasound images is a crucial means for diagnosing carotid plaques, holding significant clinical relevance for predicting the risk of stroke. Recent research suggests that utilizing plaque segmentation as an auxiliary task for classification can enhance performance by leveraging the correlation between segmentation and classification tasks. However, this approach relies on obtaining a substantial amount of challenging-to-acquire segmentation annotations. This paper proposes a novel weakly supervised auxiliary task learning network model (WAL-Net) to explore the interdependence between carotid plaque classification and segmentation tasks. The plaque classification task is primary task, while the plaque segmentation task serves as an auxiliary task, providing valuable information to enhance the performance of the primary task. Weakly supervised learning is adopted in the auxiliary task to completely break away from the dependence on segmentation annotations. Experiments and evaluations are conducted on a dataset comprising 1270 carotid plaque ultrasound images from Wuhan University Zhongnan Hospital. Results indicate that the proposed method achieved an approximately 1.3% improvement in carotid plaque classification accuracy compared to the baseline network. Specifically, the accuracy of mixed-echoic plaques classification increased by approximately 3.3%, demonstrating the effectiveness of our approach.

翻译：颈动脉超声图像的分类是诊断颈动脉斑块的关键手段，对预测卒中风险具有重要临床意义。近期研究表明，利用斑块分割作为分类的辅助任务，通过挖掘分割与分类任务间的关联性可提升分类性能。然而，该方法依赖于获取大量难以标注的分割数据。本文提出一种新型弱监督辅助任务学习网络模型（WAL-Net），旨在探索颈动脉斑块分类与分割任务间的相互依赖性。其中斑块分类为主任务，斑块分割作为辅助任务，为提升主任务性能提供关键信息。辅助任务采用弱监督学习，彻底摆脱了对分割标注的依赖。在武汉大学中南医院收集的1270幅颈动脉斑块超声图像数据集上进行了实验与评估。结果表明，与基线网络相比，所提方法使颈动脉斑块分类准确率提升约1.3%。特别地，混合回声斑块的分类准确率提升约3.3%，验证了该方法的有效性。