  Human anatomy is the foundation of medical imaging and boasts one striking
characteristic: its hierarchy in nature, exhibiting two intrinsic properties:
(1) locality: each anatomical structure is morphologically distinct from the
others; and (2) compositionality: each anatomical structure is an integrated
part of a larger whole. We envision a foundation model for medical imaging that
is consciously and purposefully developed upon this foundation to gain the
capability of "understanding" human anatomy and to possess the fundamental
properties of medical imaging. As our first step in realizing this vision
towards foundation models in medical imaging, we devise a novel self-supervised
learning (SSL) strategy that exploits the hierarchical nature of human anatomy.
Our extensive experiments demonstrate that the SSL pretrained model, derived
from our training strategy, not only outperforms state-of-the-art (SOTA)
fully/self-supervised baselines but also enhances annotation efficiency,
offering potential few-shot segmentation capabilities with performance
improvements ranging from 9% to 30% for segmentation tasks compared to SSL
baselines. This performance is attributed to the significance of anatomy
comprehension via our learning strategy, which encapsulates the intrinsic
attributes of anatomical structures-locality and compositionality-within the
embedding space, yet overlooked in existing SSL methods. All code and
pretrained models are available at https://github.com/JLiangLab/Eden.
