Papers › Learning Anatomically Consistent Embedding for Chest Radiography

Learning Anatomically Consistent Embedding for Chest Radiography

1 Dec 2023arXiv:2312.00335archive 2025-07-28

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.

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1ran · violated contract
1ran · our draft was wrong
1ran · fixture could not drive it
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Tasks

AnatomyMedical Image AnalysisSelf-Supervised Learning

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