Your browser doesn't support javascript.
loading
Quantitative chest CT assessment of pulmonary alveolar proteinosis with deep learning: a real-world longitudinal study.
Shi, Shenyun; Zou, Ruyi; Chen, Lulu; Yang, Shangwen; Xu, Kaifeng; Xin, Xiaoyan; Xiao, Yonglong.
Afiliación
  • Shi S; Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
  • Zou R; Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
  • Chen L; Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
  • Yang S; Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
  • Xu K; Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China.
  • Xin X; Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
  • Xiao Y; Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Quant Imaging Med Surg ; 12(12): 5394-5403, 2022 Dec.
Article en En | MEDLINE | ID: mdl-36465831
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Risk_factors_studies Idioma: En Revista: Quant Imaging Med Surg Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: China
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Observational_studies / Risk_factors_studies Idioma: En Revista: Quant Imaging Med Surg Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: China