Self-supervised learning (SSL) has recently achieved promising performance for 3D medical image analysis tasks. Most current methods follow existing SSL paradigm originally designed for photographic or natural images, which cannot explicitly and thoroughly exploit the intrinsic similar anatomical structures across varying medical images. This may in fact degrade the quality of learned deep representations by maximizing the similarity among features containing spatial misalignment information and different anatomical semantics. In this work, we propose a new self-supervised learning framework, namely Alice, that explicitly fulfills Anatomical invariance modeling and semantic alignment via elaborately combining discriminative and generative objectives. Alice introduces a new contrastive learning strategy which encourages the similarity between views that are diversely mined but with consistent high-level semantics, in order to learn invariant anatomical features. Moreover, we design a conditional anatomical feature alignment module to complement corrupted embeddings with globally matched semantics and inter-patch topology information, conditioned by the distribution of local image content, which permits to create better contrastive pairs. Our extensive quantitative experiments on three 3D medical image analysis tasks demonstrate and validate the performance superiority of Alice, surpassing the previous best SSL counterpart methods and showing promising ability for united representation learning. Codes are available at https://github.com/alibaba-damo-academy/alice.

翻译：自监督学习（SSL）近期在3D医学图像分析任务中展现出良好的性能。当前大多数方法沿袭了为自然图像设计的现有SSL范式，这类方法无法显式且充分地利用不同医学图像间内在相似的解剖结构。通过最大化包含空间错位信息和不同解剖语义的特征之间的相似性，实际上可能降低所学深度表征的质量。本文提出名为Alice的全新自监督学习框架，通过精心结合判别式与生成式目标，显式实现解剖不变性建模与语义对齐。Alice引入了新的对比学习策略，鼓励以多样方式挖掘但具有一致高层语义的视图之间的相似性，从而学习不变性解剖特征。此外，我们设计了条件解剖特征对齐模块，该模块以局部图像内容分布为条件，通过全局匹配的语义和补丁间拓扑信息补充受损嵌入，从而构建更优的对比对。我们在三个3D医学图像分析任务上开展的大量定量实验表明，Alice具有超越先前最佳SSL方法的性能优势，并展现出统一的表征学习能力。相关代码已开源在 https://github.com/alibaba-damo-academy/alice。