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

Abstract 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 predic...

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Autores principales: Ji Eun Park, Dain Eun, Ho Sung Kim, Da Hyun Lee, Ryoung Woo Jang, Namkug Kim
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/b9e0f4185fc740d6af0b446f7f57164e
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