Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies

Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible....

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Detalles Bibliográficos
Autores principales: Lucas, Marit, Jansen, Ilaria, Savci-Heijink, C. Dilara, Meijer, Sybren L., de Boer, Onno J., van Leeuwen, Ton G., de Bruin, Daniel M., Marquering, Henk A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611751/
https://www.ncbi.nlm.nih.gov/pubmed/31098801
http://dx.doi.org/10.1007/s00428-019-02577-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611751/
https://www.ncbi.nlm.nih.gov/pubmed/31098801
http://dx.doi.org/10.1007/s00428-019-02577-x

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