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Found 5 papers, 1 papers with code
WAL-Net: Weakly supervised auxiliary task learning network for carotid plaques classification
Specifically, the accuracy of mixed-echoic plaques classification increased by approximately 3. 3%, demonstrating the effectiveness of our approach.
A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification
The segmentation and classification of carotid plaques in ultrasound images play important roles in the treatment of atherosclerosis and assessment for the risk of stroke.
Self-training with dual uncertainty for semi-supervised medical image segmentation
In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem.
HD-GCN:A Hybrid Diffusion Graph Convolutional Network
HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix.
A Safe Semi-supervised Graph Convolution Network
Therefore, we propose a Safe GCN framework (Safe-GCN) to improve the learning performance.
