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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...
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 |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2022
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Materias: | |
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 |
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