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.

翻译：自监督掩码图像建模在自然图像上已展现出良好的效果，但直接将其应用于医学图像仍面临挑战。这一困难源于病变相对于自然图像的复杂性和独特特征，阻碍了有效的表示学习。此外，传统的高固定掩码比限制了精细病变细节的恢复，压缩了可学习信息的范围。为解决这些局限性，我们提出一种新颖的自监督医学图像分割框架——自适应掩码病变斑块（AMLP）。具体而言，我们设计了一种掩码斑块选择（MPS）策略，用于识别并聚焦于包含病变的斑块的学习。病变区域虽稀少却至关重要，因此对其精确重建至关重要。为减少MPS中无监督聚类导致的病变斑块与背景斑块的误分类，我们引入注意力重建损失（ARL），专注于可能描绘病变的难以重建的斑块。进一步提出类别一致性损失（CCL），基于重建难度细化斑块分类，增强病变与背景的区分能力。此外，我们开发了自适应掩码比（AMR）策略，逐步提高掩码比例以扩展可重建信息并提升学习效果。在两个医学分割数据集上的大量实验表明，AMLP的性能优于现有自监督方法。所提出的策略有效解决了掩码建模在医学图像应用中的局限性，针对分割任务至关重要的精细病变细节捕捉进行了定制优化。