Learning Anatomically Consistent Embedding for Chest Radiography
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911913069248512 |
|---|---|
| author | Zhou, Ziyu Luo, Haozhe Pang, Jiaxuan Ding, Xiaowei Gotway, Michael Liang, Jianming |
| author_facet | Zhou, Ziyu Luo, Haozhe Pang, Jiaxuan Ding, Xiaowei Gotway, Michael Liang, Jianming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_00335 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning Anatomically Consistent Embedding for Chest Radiography Zhou, Ziyu Luo, Haozhe Pang, Jiaxuan Ding, Xiaowei Gotway, Michael Liang, Jianming Computer Vision and Pattern Recognition 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. |
| title | Learning Anatomically Consistent Embedding for Chest Radiography |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.00335 |