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
this https URL

在本研究中，我们提出了一种对抗自我训练一致性框架 (AstMatch)，通过设计对抗一致性正则化方法增强知识传递，并在不同扰动强度下增强预测一致性，应用特征匹配损失进行对抗训练以获得高层一致性正则化，并引入金字塔通道注意力和高效通道空间注意力模块改进鉴别器性能，在众多半监督学习方法和三个公开数据集上进行广泛评估实验结果显示，AstMatch 的表现优于现有方法，达到了新的最优性能。