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.

该研究提出了一种弱监督辅助任务学习网络模型 (WAL-Net)，以探索颈动脉斑块分类与分割任务之间的相互依赖关系。在该模型中，斑块分类任务是主要任务，而斑块分割任务作为辅助任务，为主要任务提供有价值的信息以提升性能。使用弱监督学习方法，完全摆脱对分割注释的依赖。实验和评估结果表明，与基准网络相比，所提出的方法在颈动脉斑块分类准确性上获得了约 1.3% 的改进。具体而言，混合回声斑块分类准确性提高了约 3.3%，证明了我们方法的有效性。