Medical images acquired from standardized protocols show consistent macroscopic or microscopic anatomical structures, and these structures consist of composable/decomposable organs and tissues, but existing self-supervised learning (SSL) methods do not appreciate such composable/decomposable structure attributes inherent to medical images. To overcome this limitation, this paper introduces a novel SSL approach called ACE to learn anatomically consistent embedding via composition and decomposition with two key branches: (1) global consistency, capturing discriminative macro-structures via extracting global features; (2) local consistency, learning fine-grained anatomical details from composable/decomposable patch features via corresponding matrix matching. Experimental results across 6 datasets 2 backbones, evaluated in few-shot learning, fine-tuning, and property analysis, show ACE's superior robustness, transferability, and clinical potential. The innovations of our ACE lie in grid-wise image cropping, leveraging the intrinsic properties of compositionality and decompositionality of medical images, bridging the semantic gap from high-level pathologies to low-level tissue anomalies, and providing a new SSL method for medical imaging.

翻译：采用标准化协议获取的医学图像展现出宏观或微观解剖结构的一致性，这些结构由可组合/可分解的器官与组织构成。然而，现有的自监督学习方法未能充分重视医学图像内在的这种可组合/可分解结构属性。为克服此局限，本文提出一种新颖的自监督学习方法ACE，通过组合与分解学习解剖学一致性嵌入，其包含两个关键分支：(1) 全局一致性分支，通过提取全局特征捕捉具有判别性的宏观结构；(2) 局部一致性分支，通过对应矩阵匹配从可组合/可分解的局部图像块特征中学习细粒度解剖细节。在6个数据集和2种骨干网络上进行的实验，涵盖小样本学习、微调及特性分析，结果表明ACE具有优异的鲁棒性、可迁移性及临床潜力。本方法ACE的创新点在于：采用网格化图像裁剪策略，利用医学图像固有的组合与分解特性，弥合从高层次病理到低层次组织异常的语义鸿沟，从而为医学影像提供了一种新的自监督学习方法。