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

TL;DR

SemiGDA introduces a dual-distribution alignment framework with a novel consistency-driven skip adapter to improve semi-supervised medical image segmentation, especially with limited labeled data.

Contribution

It proposes a dual-encoder distribution alignment and a consistency-driven skip adapter to enhance semantic learning and feature consistency in semi-supervised segmentation.

Findings

Outperforms state-of-the-art semi-supervised segmentation methods on medical datasets.

Effectively models feature and semantic distributions with limited labeled data.

Enhances semantic consistency through a novel skip connection strategy.

Abstract

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.