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

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...

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Detalles Bibliográficos
Autores principales: Kalantar, Reza, Hindocha, Sumeet, Hunter, Benjamin, Sharma, Bhupinder, Khan, Nasir, Koh, Dow-Mu, Ahmed, Merina, Aboagye, Eric O., Lee, Richard W., Blackledge, Matthew D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10310777/
https://www.ncbi.nlm.nih.gov/pubmed/37386097
http://dx.doi.org/10.1038/s41598-023-36712-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10310777/
https://www.ncbi.nlm.nih.gov/pubmed/37386097
http://dx.doi.org/10.1038/s41598-023-36712-1

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