SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation
For medical image segmentation with limited labels, SemiGDA provides a novel framework that improves semantic learning and scene adaptability, achieving superior performance over existing methods.
SemiGDA addresses the limitation of traditional discriminative semi-supervised medical image segmentation methods that neglect feature-level distribution constraints, by aligning feature and semantic distributions via a Dual-distribution Alignment Module and a Consistency-Driven Skip Adapter. It outperforms state-of-the-art semi-supervised segmentation methods on diverse medical datasets.
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. To address these limitations, we propose SemiGDA, a novel Generative Dual-distribution Alignment framework for semi-supervised medical image segmentation. Our SemiGDA overcomes the reliance of discriminative methods on large labeled datasets by aligning feature and semantic distributions to boost semantic learning and scene adaptability. Specifically, we propose a Dual-distribution Alignment Module (DAM), which employs two structurally distinct encoders to model image and mask feature distributions. It enforces their alignment in the latent space via distributional constraints, establishing structured feature consistency. Moreover, we design a Consistency-Driven Skip Adapter (CDSA) strategy, which introduces dual skip adapters (Image and Mask) to fuse multi-scale features via skip connections. Using a consistency loss, CDSA enhances cross-branch semantic alignment and reinforces fine-grained semantic consistency. Experimental results on diverse medical datasets show that our method outperforms other state-of-the-art semi-supervised segmentation methods. Code is released at: https://github.com/taozh2017/SemiGDA.