Semi-supervised learning (SSL) has shown considerable potential in medical image segmentation, primarily leveraging consistency regularization and pseudo-labeling. However, many SSL approaches only pay attention to low-level consistency and overlook the significance of pseudo-label reliability. Therefore, in this work, we propose an adversarial self-training consistency framework (AstMatch). Firstly, we design an adversarial consistency regularization (ACR) approach to enhance knowledge transfer and strengthen prediction consistency under varying perturbation intensities. Second, we apply a feature matching loss for adversarial training to incorporate high-level consistency regularization. Additionally, we present the pyramid channel attention (PCA) and efficient channel and spatial attention (ECSA) modules to improve the discriminator's performance. Finally, we propose an adaptive self-training (AST) approach to ensure the pseudo-labels' quality. The proposed AstMatch has been extensively evaluated with cutting-edge SSL methods on three public-available datasets. The experimental results under different labeled ratios indicate that AstMatch outperforms other existing methods, achieving new state-of-the-art performance. Our code will be available at https://github.com/GuanghaoZhu663/AstMatch.

翻译：半监督学习（SSL）在医学图像分割领域展现出巨大潜力，其核心在于利用一致性正则化和伪标签技术。然而，现有许多SSL方法仅关注低层次的一致性，而忽视了伪标签可靠性的重要性。为此，本文提出一种对抗性自训练一致性框架（AstMatch）。首先，我们设计了一种对抗性一致性正则化（ACR）方法，以增强知识迁移能力，并在不同扰动强度下强化预测一致性。其次，我们在对抗训练中引入特征匹配损失，以融入高层次的一致性正则化。此外，我们提出了金字塔通道注意力（PCA）模块与高效通道空间注意力（ECSA）模块，以提升判别器的性能。最后，我们提出一种自适应自训练（AST）方法，以确保伪标签的质量。所提出的AstMatch框架已在三个公开数据集上，与前沿SSL方法进行了广泛对比评估。在不同标注比例下的实验结果表明，AstMatch优于其他现有方法，取得了新的最先进性能。我们的代码将在 https://github.com/GuanghaoZhu663/AstMatch 公开。