SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation

ArXi:2604.23274v1 Announce Type: new Semi-supervised learning addresses label scarcity and high annotation costs in medical image segmentation by exploiting the latent information in unlabeled data to enhance model performance. Traditional discriminative segmentation relies on segmentation masks, neglecting feature-level distribution constraints. This limits robust semantic representation learning and adaptive modeling of unlabeled data in scenarios with few labels.