Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-G...
Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomography (CT) imaging-based artificial intelligence models for COVID-19 research. However, contrast heterogeneity from real-world datasets may impair model performance. Contrast-homogenous datasets present a potential solution. We developed a 3D patch-bas...
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Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19
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TN_cdi_doaj_primary_oai_doaj_org_article_8fb53933387e4dbfb8ae4ffbefa7d5b2
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_8fb53933387e4dbfb8ae4ffbefa7d5b2
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-023-36712-1
