In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively
exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ …

For medical image segmentation, contrastive learning is the dominant practice to improve
the quality of visual representations by contrasting semantically similar and dissimilar pairs …

Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in medical image segmentation. Existing methods mainly focus on …

Volume-wise labeling in 3D medical images is a time-consuming task that requires
expertise. As a result, there is growing interest in using semi-supervised learning (SSL) …

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …

Weakly supervised point cloud segmentation, ie semantically segmenting a point cloud with
only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden …

We propose a novel teacher-student model for semi-supervised multi-organ segmentation.
In the teacher-student model, data augmentation is usually adopted on unlabeled data to …

Medical data often exhibits long-tail distributions with heavy class imbalance, which
naturally leads to difficulty in classifying the minority classes (ie, boundary regions or rare …

Semi-supervised learning has garnered significant interest as a method to alleviate the
burden of data annotation. Recently, semi-supervised medical image segmentation has …