Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks

PURPOSE: To suggest an attention-aware, cycle-consistent generative adversarial network (A-CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to current state-of-the-art MR-to-CT image translation methods. MATERIALS AND METHODS: An attention-gating mechanism is incorpor...

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Detalhes bibliográficos
Publicado no:Radiol Artif Intell
Main Authors: Kearney, Vasant, Ziemer, Benjamin P., Perry, Alan, Wang, Tianqi, Chan, Jason W., Ma, Lijun, Morin, Olivier, Yom, Sue S., Solberg, Timothy D.
Formato: Artigo
Idioma:Inglês
Publicado em: Radiological Society of North America 2020
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Original Research
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC8017410/
https://ncbi.nlm.nih.gov/pubmed/33937817
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1148/ryai.2020190027
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