Synthetic chest X-ray images from text prompts
A latent diffusion model pre-trained on pairs of natural images and text descriptors can be adapted to generate realistic chest radiographs that are controlled by free-form medical text prompts.
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
07 October 2024
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| In: |
Nature biomedical engineering
Year: 2025, Volume: 9, Issue: 4, Pages: 439-440 |
| ISSN: | 2157-846X |
| DOI: | 10.1038/s41551-024-01261-z |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41551-024-01261-z Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41551-024-01261-z 
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| Author Notes: | Daniel Truhn, Jakob Nikolas Kather |
| Summary: | A latent diffusion model pre-trained on pairs of natural images and text descriptors can be adapted to generate realistic chest radiographs that are controlled by free-form medical text prompts. |
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| Item Description: | Gesehen am 29.04.2025 |
| Physical Description: | Online Resource |
| ISSN: | 2157-846X |
| DOI: | 10.1038/s41551-024-01261-z |