A vision–language foundation model for the generation of realistic chest X-ray images
Open accessAbstract
The paucity of high-quality medical imaging datasets could be mitigated by machine learning models that generate compositionally diverse images that faithfully represent medical concepts and pathologies. However, large vision-language models are trained on natural images, and the diversity distribution of the generated images substantially differs from that of medical images. Moreover, medical language involves specific and semantically rich vocabulary. Here we describe a domain-adaptation strategy for large vision-language models that overcomes distributional shifts. Specifically, by leveraging publicly available datasets of chest X-ray images and the corresponding radiology reports, we adapted a latent diffusion model pre-trained on pairs of natural images and text descriptors to generate diverse and visually plausible synthetic chest X-ray images (as confirmed by board-certified radiologists) whose appearance can be controlled with free-form medical text prompts. The domain-adaptation strategy for the text-conditioned synthesis of medical images can be used to augment training datasets and is a viable alternative to the sharing of real medical images for model training and fine-tuning.
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Generative AI models such as large language models or vision-language models capable of synthesizing images allow for tailored data augmentation by creating new examples that meet targeted criteria.
“Generative AI models, such as large language models or vision-language models capable of synthesizing images, additionally allow for tailored data augmentation by creating new examples that meet a set of targeted criteria.71, 72, 73”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [72]
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- Authors
- Bluethgen, C., Chambon, P., Delbrouck, J.-B., van der Sluijs, R., Połacin, M., Zambrano Chaves, J. M., Abraham, T. M., Purohit, S., Langlotz, C. P., & Chaudhari, A. S.
- Affiliations
- Stanford University (US)
- University of California, Davis (US)
- ORCID
- Venue
- Nature Biomedical Engineering
- Published
- 2024-08-26
- Volume
- 9
- Issue
- 4
- Pages
- 494-506
- ISSN
- 2157-846X (ISSN-L)
- PMC
- PMC11861387
- OpenAlex
- W4401888965
- Cited by
- 108
- References
- 43
- Citations by year
- 2026: 48 · 2025: 54 · 2024: 6
- FWCI
- 15.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Funders
- Schweizerische Gesellschaft für Radiologie
- National Institutes of Health (75N92020C00021, 75N92020C00008, AR077604, EB002524)
- National Heart, Lung, and Blood Institute (75N92020C00008, 75N92020C00021, R01HL169345, R01HL167974)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR077604, R01AR079431)
- National Institute of Biomedical Imaging and Bioengineering (75N92020C00021, EB 002524, 75N92020C00008, P41EB027060, R01EB002524)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- vision-language foundation models, latent diffusion models, domain adaptation, distribution shift, radiology reports, medical image synthesis, data augmentation, medical image dataset
- MeSH terms
- Machine Learning, Algorithms, Humans, Image Processing, Computer-Assisted, Language, Radiography, Thoracic, Thorax, Databases, Factual
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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