Self-supervised learning (SSL) approaches have recently shown substantial
success in learning visual representations from unannotated images. Compared
with photographic images, medical images acquired with the same imaging
protocol exhibit high consistency in anatomy. To exploit this anatomical
consistency, this paper introduces a novel SSL approach, called PEAC (patch
embedding of anatomical consistency), for medical image analysis. Specifically,
in this paper, we propose to learn global and local consistencies via stable
grid-based matching, transfer pre-trained PEAC models to diverse downstream
tasks, and extensively demonstrate that (1) PEAC achieves significantly better
performance than the existing state-of-the-art fully/self-supervised methods,
and (2) PEAC captures the anatomical structure consistency across views of the
same patient and across patients of different genders, weights, and healthy
statuses, which enhances the interpretability of our method for medical image
analysis.

本文针对医学图像分析引入了一种新的自监督学习方法 PEAC（解剖一致性的图块嵌入），通过稳定的基于网格的匹配学习全局和局部一致性，并广泛证明了 PEAC 相比现有最先进的完全 / 自监督方法在性能上明显更好，而且能够捕捉同一患者和不同性别、体重、健康状况的患者视图中的解剖结构一致性，提升了我们方法在医学图像分析中的可解释性。