Asymmetric dual-path structural consistency for semi-supervised medical image segmentation
Deep neural networks have achieved remarkable progress in medical image segmentation, but their performance still depends heavily on large-scale pixel-level annotations. Semi-supervised learning (SSL) alleviates this burden by leveraging unlabeled data, yet conventional teacher–student frameworks often suffer from erro...