Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports
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Paired image-text co-learning has been used to learn from chest X-rays and their associated text reports, outperforming other self-supervised and fully supervised methods.
“This approach for paired image-text co-learning has been used recently to learn from chest X-rays and their associated text reports, outperforming other self-supervised and fully supervised methods 108”
From Multimodal biomedical AI (2022) · cites this paper as [108]
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- Authors
- Zhou, H.-Y., Chen, X., Zhang, Y., Luo, R., Wang, L., & Yu, Y.
- Affiliations
- University of Hong Kong (HK)
- Xiamen University (CN)
- Corresponding author
- Wang, Liansheng
- Yu, Yizhou
- Venue
- Nature Machine Intelligence
- Published
- 2022-01-20
- Volume
- 4
- Issue
- 1
- Pages
- 32-40
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3213233983
- Cited by
- 126
- References
- 45
- Citations by year
- 2026: 19 · 2025: 39 · 2024: 28 · 2023: 30 · 2022: 9
- FWCI
- 8.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Fundamental Research Funds for the Central Universities (20720210121, 20720190012)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- radiology reports, weakly supervised learning, vision transformer, transfer learning, self-supervised learning, X-ray images, limited supervision
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