Existing self-supervised learning methods based on contrastive learning and masked image modeling have demonstrated impressive performances. However, current masked image modeling methods are mainly utilized in natural images, and their applications in medical images are relatively lacking. Besides, their fixed high masking strategy limits the upper bound of conditional mutual information, and the gradient noise is considerable, making less the learned representation information. Motivated by these limitations, in this paper, we propose masked patches selection and adaptive masking strategy based self-supervised medical image segmentation method, named MPS-AMS. We leverage the masked patches selection strategy to choose masked patches with lesions to obtain more lesion representation information, and the adaptive masking strategy is utilized to help learn more mutual information and improve performance further. Extensive experiments on three public medical image segmentation datasets (BUSI, Hecktor, and Brats2018) show that our proposed method greatly outperforms the state-of-the-art self-supervised baselines.

翻译：现有基于对比学习和掩码图像建模的自监督学习方法已展现出显著性能。然而，当前掩码图像建模方法主要应用于自然图像，在医学图像领域的应用相对匮乏。此外，其固定的高掩码策略限制了条件互信息的上限，且梯度噪声显著，导致所学表征信息不足。针对这些局限，本文提出基于掩码补丁选择与自适应掩码策略的自监督医学图像分割方法MPS-AMS。我们利用掩码补丁选择策略选取包含病灶的掩码补丁以获取更多病灶表征信息，同时采用自适应掩码策略帮助学习更多互信息并进一步提升性能。在三个公开医学图像分割数据集（BUSI、Hecktor和Brats2018）上的大量实验表明，我们提出的方法显著优于当前最先进的自监督基线方法。