Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs

PURPOSE: To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. MATERIALS AND METHODS: This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015–2017 for training. Anteroposterior virtual chest, lung, and pn...

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Detalles Bibliográficos
Autores principales: Yoo, Seung-Jin, Kim, Hyungjin, Witanto, Joseph Nathanael, Inui, Shohei, Yoon, Jeong-Hwa, Lee, Ki-Deok, Choi, Yo Won, Goo, Jin Mo, Yoon, Soon Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181872/
https://www.ncbi.nlm.nih.gov/pubmed/37209462
http://dx.doi.org/10.1016/j.ejrad.2023.110858

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181872/
https://www.ncbi.nlm.nih.gov/pubmed/37209462
http://dx.doi.org/10.1016/j.ejrad.2023.110858

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