Generation of synthetic ground glass nodules using generative adversarial networks (GANs)

BACKGROUND: Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs). METHODS: We used 216 computed tomography images with 340 GGNs from the L...

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Detalles Bibliográficos
Autores principales: Wang, Zhixiang, Zhang, Zhen, Feng, Ying, Hendriks, Lizza E. L., Miclea, Razvan L., Gietema, Hester, Schoenmaekers, Janna, Dekker, Andre, Wee, Leonard, Traverso, Alberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708993/
https://www.ncbi.nlm.nih.gov/pubmed/36447082
http://dx.doi.org/10.1186/s41747-022-00311-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708993/
https://www.ncbi.nlm.nih.gov/pubmed/36447082
http://dx.doi.org/10.1186/s41747-022-00311-y

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