AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation
<|reference_start|>AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation: 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.<|reference_end|>
@article{zhu2024astmatch:,
    title={AstMatch: Adversarial Self-training Consistency Framework for
  Semi-Supervised Medical Image Segmentation},
    author={Guanghao Zhu and Jing Zhang and Juanxiu Liu and Xiaohui Du and Ruqian
  Hao and Yong Liu and Lin Liu},
    journal={arXiv preprint arXiv:2406.19649},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19649},
    primaryClass={eess.IV cs.CV}
}