WAL-Net: Weakly supervised auxiliary task learning network for carotid plaques classification
This work addresses the challenge of reducing reliance on hard-to-obtain segmentation annotations in medical imaging for clinicians, though it is incremental as it builds on prior methods using auxiliary tasks.
The paper tackled the problem of classifying carotid artery ultrasound images for stroke risk prediction by proposing WAL-Net, a weakly supervised model that uses plaque segmentation as an auxiliary task without needing segmentation annotations, resulting in a 1.3% overall accuracy improvement and a 3.3% increase for mixed-echoic plaques.
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.