A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides

Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of renal allograft rejection. A fully automated system u...

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Detalles Bibliográficos
Autores principales: Kim, Young-Gon, Choi, Gyuheon, Go, Heounjeong, Cho, Yongwon, Lee, Hyunna, Lee, A-Reum, Park, Beomhee, Kim, Namkug
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435691/
https://www.ncbi.nlm.nih.gov/pubmed/30914690
http://dx.doi.org/10.1038/s41598-019-41479-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6435691/
https://www.ncbi.nlm.nih.gov/pubmed/30914690
http://dx.doi.org/10.1038/s41598-019-41479-5

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