Cargando…
Progressive U-Net residual network for computed tomography images super-resolution in the screening of COVID-19
Thin-slice computed tomography (CT) examination plays an important role in the screening of suspected and confirmed coronavirus disease 2019 (COVID-19) outbreak patients. Therefore, improving the image resolution of COVID-19 CT has important clinical value for the diagnosis and condition assessment...
Autores principales: | Qiu, Defu, Cheng, Yuhu, Wang, Xuesong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
THE AUTHORS. Published by Elsevier BV on behalf of Egyptian Society of Radiation Science and Applications (ESRSA).
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760384/ http://dx.doi.org/10.1080/16878507.2021.1973760 |
Ejemplares similares
-
Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images
por: Qiu, Defu, et al.
Publicado: (2021) -
Cardiac Magnetic Resonance Images Superresolution via Multichannel Residual Attention Networks
por: Qiu, Defu, et al.
Publicado: (2021) -
Super-Resolution Residual U-Net Model for the
Reconstruction of Limited-Data Tunable Diode Laser Absorption Tomography
por: Chen, Shaogang, et al.
Publicado: (2022) -
Image Super-Resolution via Dual-Level Recurrent Residual Networks
por: Tan, Congming, et al.
Publicado: (2022) -
Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network
por: Chen, Qian, et al.
Publicado: (2022)