Abstract
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 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-supervised and self-supervised methods, and (2) PEAC effectively captures the anatomical structure consistency between patients of different genders and weights and between different views of the same patient, which enhances the interpretability of our method for medical image analysis. All code and pretrained models are available at (censored).Video
Citation
@inproceedings{Zhou_2023_BMVC,
author = {Ziyu Zhou and Haozhe Luo and Jiaxuan Pang and xiaowei ding and Michael Gotway and Jianming Liang},
title = {Learning Anatomically Consistent Embedding for Chest Radiography},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0617.pdf}
}
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