AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation
This work addresses the challenge of improving segmentation accuracy with limited labeled data in medical imaging, which is crucial for clinical applications, though it appears incremental by building on existing SSL methods.
The paper tackles the problem of unreliable pseudo-labels in semi-supervised medical image segmentation by proposing AstMatch, a framework that integrates adversarial consistency regularization and adaptive self-training, achieving state-of-the-art performance on three public datasets under various labeled ratios.
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.