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- W4385376498 abstract "Carotid plaque classification holds significant importance in the clinical practice for evaluating the risk of coronary artery disease (CAD) in patients. While deep supervised learning has been proven to be effective in carotid plaque classification, its performance is limited by the scarcity of labeled images of carotid plaques that are available in clinical settings. To improve the classification performance of convolutional neural networks (CNNs) on a small dataset of carotid plaques, we propose a carotid plaque classification method based on self-supervised triple-view contrastive learning called SSTVC. First, in the pretext task, we use the triplet network to input three different data-augmented views for the model and combine the contrastive loss function based on redundancy reduction to better learn the visual representation of carotid plaque. Then, the pretrained encoder network parameters in the pretext task are transferred to the downstream classification task as the initialization parameters of the classification network. We train and test the classification network on different numbers of labeled carotid plaque ultrasound image datasets to evaluate its classification performance. Experimental results show that our method can effectively improve the classification performance of CNNs on carotid plaque ultrasound images with limited labeled data, which may help clinical doctors diagnose carotid plaque categories and evaluate patients’ risks of CAD." @default.
- W4385376498 created "2023-07-30" @default.
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- W4385376498 date "2023-01-01" @default.
- W4385376498 modified "2023-09-28" @default.
- W4385376498 title "SSTVC: Carotid Plaque Classification from Ultrasound Images Using Self-supervised Triple-View Contrast Learning" @default.
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- W4385376498 doi "https://doi.org/10.1007/978-981-99-4749-2_13" @default.
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