Medical image segmentation is a pivotal step in diagnostic and therapeutic processes. However, the acquisition of high-quality annotated data is often constrained by scarcity and cost. Semi-supervised learning offers a promising approach to enhance model performance by using unlabeled data. While consistency regularization is a prevalent method in semi-supervised image segmentation, there is a dearth of research on perturbation strategies tailored for semi-supervised medical image segmentation tasks. This paper introduces an attention-guided perturbation strategy for semi-supervised consistency regularization in the context of medical image segmentation. We add the perturbation based on the attention from the model in the image and feature level to achieve consistency regularization. The method is adept at accommodating the intricate structures and high-dimensional semantics inherent in medical images, thereby enhancing the performance of semi-supervised segmentation tasks. Our method achieved state-of-the-art results on benchmark datasets, including a 90.4\% Dice score on the ACDC dataset in the 7-case scenario.

本研究解决了半监督医学图像分割中关于扰动策略的不足。提出了一种基于模型注意力的扰动方法，通过增强一致性正则化来处理医学图像的复杂结构和高维语义。实验结果表明，该方法在基准数据集上取得了最先进的性能，ACDC数据集的Dice分数达到90.4%。

面向一致性正则化的注意力引导扰动在半监督医学图像分割中的应用