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 this https URL

通过自我监督学习策略，利用人体解剖的分级性质和组成性，该模型提供了与全 / 自我监督方法相比改进了 9% 到 30% 的分割任务性能以及增强了注释效率，并且能够更好地理解人体解剖学。

基于自监督学习从解剖学中学习的医学成像基础模型

Towards Foundation Models Learned from Anatomy in Medical Imaging via  Self-Supervision

We present a novel generative model, called Bidirectional GaitNet, that
learns the relationship between human anatomy and its gait. The simulation
model of human anatomy is a comprehensive, full-body, simulation-ready,
musculoskeletal model with 304 Hill-type musculotendon units. The Bidirectional
GaitNet consists of forward and backward models. The forward model predicts a
gait pattern of a person with specific physical conditions, while the backward
model estimates the physical conditions of a person when his/her gait pattern
is provided. Our simulation-based approach first learns the forward model by
distilling the simulation data generated by a state-of-the-art predictive gait
simulator and then constructs a Variational Autoencoder (VAE) with the learned
forward model as its decoder. Once it is learned its encoder serves as the
backward model. We demonstrate our model on a variety of healthy/impaired gaits
and validate it in comparison with physical examination data of real patients.

本文提出了一种名为 Bidirectional GaitNet 的新型生成模型，它可以学习人体解剖结构和步态之间的关系，并在 304 个 Hill 型肌腱单位的情况下以正向和反向模型进行训练和预测。