Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging

Thus far, there have been no reported specific rules for systematically determining the appropriate augmented sample size to optimize model performance when conducting data augmentation. In this paper, we report on the feasibility of synthetic data augmentation using generative adversarial networks...

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Detalles Bibliográficos
Autores principales: Kong, Hyoun-Joong, Kim, Jin Youp, Moon, Hye-Min, Park, Hae Chan, Kim, Jeong-Whun, Lim, Ruth, Woo, Jonghye, Fakhri, Georges El, Kim, Dae Woo, Kim, Sungwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613909/
https://www.ncbi.nlm.nih.gov/pubmed/36302815
http://dx.doi.org/10.1038/s41598-022-22222-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613909/
https://www.ncbi.nlm.nih.gov/pubmed/36302815
http://dx.doi.org/10.1038/s41598-022-22222-z

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