  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.
