Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type

Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic variations to improve molecular-based prediction, as...

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Detalles Bibliográficos
Autores principales: Park, Ji Eun, Eun, Dain, Kim, Ho Sung, Lee, Da Hyun, Jang, Ryoung Woo, Kim, Namkug
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110557/
https://www.ncbi.nlm.nih.gov/pubmed/33972663
http://dx.doi.org/10.1038/s41598-021-89477-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110557/
https://www.ncbi.nlm.nih.gov/pubmed/33972663
http://dx.doi.org/10.1038/s41598-021-89477-w

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