Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease

OBJECTIVE: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated...

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Detalles Bibliográficos
Autores principales: Hwang, Hye Jeon, Kim, Hyunjong, Seo, Joon Beom, Ye, Jong Chul, Oh, Gyutaek, Lee, Sang Min, Jang, Ryoungwoo, Yun, Jihye, Kim, Namkug, Park, Hee Jun, Lee, Ho Yun, Yoon, Soon Ho, Shin, Kyung Eun, Lee, Jae Wook, Kwon, Woocheol, Sun, Joo Sung, You, Seulgi, Chung, Myung Hee, Gil, Bo Mi, Lim, Jae-Kwang, Lee, Youkyung, Hong, Su Jin, Choi, Yo Won
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2023
Materias:
Thoracic Imaging
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10400368/
https://www.ncbi.nlm.nih.gov/pubmed/37500581
http://dx.doi.org/10.3348/kjr.2023.0088

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10400368/
https://www.ncbi.nlm.nih.gov/pubmed/37500581
http://dx.doi.org/10.3348/kjr.2023.0088

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