Key result
SAL-Net improves plaque classification performance by leveraging correlations with semantic segmentation tasks.
Why the study?
Prior research has disregarded relationships between various carotid plaque tasks, and datasets with substantial manually annotated plaque regions are scarce due to high costs.
A novel semi-supervised auxiliary learning network (SAL-Net) improves carotid plaque classification performance by leveraging the correlation between plaque classification and semantic segmentation tasks.
May aid computational carotid imaging research; leaves open prospective clinical validation before adoption.
The analysis of plaque region in carotid ultrasound images is crucial for determining and assessing the harm-fulness of carotid plaques. Carotid ultrasound images provide both the location and status information of plaques, which can help diagnose carotid atherosclerosis. Despite this, the relationship between various plaque tasks has been disregarded in prior research, and due to the significant expense associated with manual image segmentation, there is a shortage of datasets that contain a substantial quantity of manually annotated plaque regions. In this paper, a semi-supervised learning algorithm is proposed to reduce reliance on annotated data, and due to the correlation between the plaque classification task and the plaque region semantic segmentation task, an auxiliary learning method named SAL-Net is proposed. The primary task of this model is supervised plaque classification, while the auxiliary task is a semi-supervised semantic segmentation task. The experiments are carried out on a carotid ultrasound image dataset, and the results show that SAL-net can effectively utilize the correlation between different tasks to improve the performance of the model.
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Fu et al. (2023) studied Carotid plaques. SAL-Net (Semi-Supervised Auxiliary Learning Network) was evaluated on Plaque classification performance. A semi-supervised auxiliary learning network (SAL-Net) effectively utilized the correlation between plaque classification and semantic segmentation tasks to improve model performance.
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