  Self-supervised masked image modeling has shown promising results on natural
images. However, directly applying such methods to medical images remains
challenging. This difficulty stems from the complexity and distinct
characteristics of lesions compared to natural images, which impedes effective
representation learning. Additionally, conventional high fixed masking ratios
restrict reconstructing fine lesion details, limiting the scope of learnable
information. To tackle these limitations, we propose a novel self-supervised
medical image segmentation framework, Adaptive Masking Lesion Patches (AMLP).
Specifically, we design a Masked Patch Selection (MPS) strategy to identify and
focus learning on patches containing lesions. Lesion regions are scarce yet
critical, making their precise reconstruction vital. To reduce
misclassification of lesion and background patches caused by unsupervised
clustering in MPS, we introduce an Attention Reconstruction Loss (ARL) to focus
on hard-to-reconstruct patches likely depicting lesions. We further propose a
Category Consistency Loss (CCL) to refine patch categorization based on
reconstruction difficulty, strengthening distinction between lesions and
background. Moreover, we develop an Adaptive Masking Ratio (AMR) strategy that
gradually increases the masking ratio to expand reconstructible information and
improve learning. Extensive experiments on two medical segmentation datasets
demonstrate AMLP's superior performance compared to existing self-supervised
approaches. The proposed strategies effectively address limitations in applying
masked modeling to medical images, tailored to capturing fine lesion details
vital for segmentation tasks.
