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

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

Abstract 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 dataset...

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Bibliographic Details
Main Authors: Reza Kalantar, Sumeet Hindocha, Benjamin Hunter, Bhupinder Sharma, Nasir Khan, Dow-Mu Koh, Merina Ahmed, Eric O. Aboagye, Richard W. Lee, Matthew D. Blackledge
Format: Article
Language:English
Published: Nature Portfolio 2023-06-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-36712-1

Internet

https://doi.org/10.1038/s41598-023-36712-1

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