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Perceived Realism of High-Resolution Generative Adversarial Network–derived Synthetic Mammograms
PURPOSE: To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts. MATERIALS AND METHODS: In this retrospective study, progressive growing GANs were used to synthesize mammograms at a re...
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| Publicado no: | Radiol Artif Intell |
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| Main Authors: | , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Radiological Society of North America
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8043361/ https://ncbi.nlm.nih.gov/pubmed/33937856 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1148/ryai.2020190181 |
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