  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.
