AstMatch：用于半监督医学图像分割的对抗性自训练一致性框架
AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation
AI总结：
本文提出AstMatch框架，通过对抗一致性正则化、特征匹配损失、注意力模块和自适应自训练提升半监督医学图像分割中伪标签质量与分割性能，在三个公开数据集上取得最优结果。
摘要：
半监督学习（SSL）在医学图像分割中展现出相当大的潜力，主要利用一致性正则化和伪标签。然而，许多SSL方法只关注低级一致性，而忽视了伪标签可靠性的重要性。因此，在本工作中，我们提出了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一致性正则化（ACR）方法，以增强知识迁移并在不同扰动强度下加强预测一致性。其次，我们应用特征匹配损失进行对抗训练，以引入高级一致性正则化。此外，我们提出了金字塔通道注意力（PCA）和高效通道与空间注意力（ECSA）模块来提升判别器的性能。最后，我们提出了一种自适应自训练（AST）方法以确保伪标签的质量。所提出的AstMatch已在三个公开可用的数据集上与前沿SSL方法进行了广泛评估。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 提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
