Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT...
Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
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Publisher
London: Nature Publishing Group UK
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Language
English
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London: Nature Publishing Group UK
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Scope and Contents
Contents
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 data augmentation. We evaluate the use of CycleGAN...
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Full title
Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks
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TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6858365
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6858365
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-019-52737-x
