Learning Anatomically Consistent Embedding for Chest Radiography
Ziyu Zhou Haozhe Luo Jiaxuan Pang Xiaowei Ding Michael Gotway Jianming Liang
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.