Cargando…
Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation
Motivated by the lack of publicly available datasets of chest radiographs of positive patients with coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveragin...
Autores principales: | Zunair, Hasib, Hamza, A. Ben |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Vienna
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7903408/ https://www.ncbi.nlm.nih.gov/pubmed/33643491 http://dx.doi.org/10.1007/s13278-021-00731-5 |
Ejemplares similares
-
Correction to: Unpaired Image-to-Image Translation Using Adversarial Consistency Loss
por: Zhao, Yihao, et al.
Publicado: (2021) -
CryoETGAN: Cryo-Electron Tomography Image Synthesis via Unpaired Image Translation
por: Wu, Xindi, et al.
Publicado: (2022) -
DeSmoke-LAP: improved unpaired image-to-image translation for desmoking in laparoscopic surgery
por: Pan, Yirou, et al.
Publicado: (2022) -
Unpaired MR-CT brain dataset for unsupervised image translation
por: Al-Kadi, Omar S., et al.
Publicado: (2022) -
COVID-19 prognosis using limited chest X-ray images
por: Mondal, Arnab Kumar
Publicado: (2022)