Glomerular disease classification and lesion identification by machine learning

BACKGROUND: Classification of glomerular diseases and identification of glomerular lesions require careful morphological examination by experienced nephropathologists, which is labor-intensive, time-consuming, and prone to interobserver variability. In this regard, recent advance in machine learning...

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Detalles Bibliográficos
Autores principales: Yang, Cheng-Kun, Lee, Ching-Yi, Wang, Hsiang-Sheng, Huang, Shun-Chen, Liang, Peir-In, Chen, Jung-Sheng, Kuo, Chang-Fu, Tu, Kun-Hua, Yeh, Chao-Yuan, Chen, Tai-Di
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Chang Gung University 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486238/
https://www.ncbi.nlm.nih.gov/pubmed/34506971
http://dx.doi.org/10.1016/j.bj.2021.08.011

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486238/
https://www.ncbi.nlm.nih.gov/pubmed/34506971
http://dx.doi.org/10.1016/j.bj.2021.08.011

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