  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.
