WAL-Net: Weakly supervised auxiliary task learning network for carotid plaques classification

• Published in: Engineering applications of artificial intelligence 2024-11, Vol.137, p.109144, Article 109144
• Main Authors: Gan, Haitao, Fu, Lingchao, Zhou, Ran, Gan, Weiyan, Wang, Furong, Wu, Xiaoyan, Yang, Zhi, Huang, Zhongwei
• Format: Article
• Language: English
• Subjects: Auxiliary-task learning; Carotid plaque classification; Weakly supervised segmentation
• ISSN: 0952-1976
• DOI: 10.1016/j.engappai.2024.109144

The classification of carotid artery ultrasound images is a crucial step for diagnosing carotid plaques, holding significant clinical relevance for predicting the risk of stroke. Recent research suggests that utilizing supervised 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 the primary one, while the plaque segmentation task serves as the auxiliary one, providing valuable information to enhance the performance of the primary task. Weakly supervised learning is adopted in the auxiliary task in which the segmentation masks are not provided. Experiments and evaluations are conducted on a dataset comprising 1270 carotid plaque ultrasound images from Wuhan University Zhongnan Hospital. Results indicate that WAL-Net 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 WAL-Net.