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Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
Labeled medical imaging data is scarce and expensive to generate. To achieve generalizable deep learning models large amounts of data are needed. Standard data augmentation is a method to increase generalizability and is routinely performed. Generative adversarial networks offer a novel method for d...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6858365/ https://www.ncbi.nlm.nih.gov/pubmed/31729403 http://dx.doi.org/10.1038/s41598-019-52737-x |