AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2406.19649v1
• Date: Fri, 28 Jun 2024 04:38:12 GMT
• Title: AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation
• Authors: Guanghao Zhu, Jing Zhang, Juanxiu Liu, Xiaohui Du, Ruqian Hao, Yong Liu, Lin Liu,
• Abstract summary: Semi-supervised learning (SSL) has shown considerable potential in medical image segmentation. In this work, we propose an adversarial self-training consistency framework (AstMatch) The proposed AstMatch has been extensively evaluated with cutting-edge SSL methods on three public-available datasets.
• Score: 19.80612796391153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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