Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network

Purpose: Prostate cancer primarily arises from the glandular epithelium. Histomophometric techniques have been used to assess the glandular epithelium in automated detection and classification pipelines; however, they are often rigid in their implementation, and their performance suffers on large da...

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Detalhes bibliográficos
Publicado no:J Med Imaging (Bellingham)
Main Authors: Bukowy, John D., Foss, Halle, McGarry, Sean D., Lowman, Allison K., Hurrell, Sarah L., Iczkowski, Kenneth A., Banerjee, Anjishnu, Bobholz, Samuel A., Barrington, Alexander, Dayton, Alex, Unteriner, Jackson, Jacobsohn, Kenneth, See, William A., Nevalainen, Marja T., Nencka, Andrew S., Ethridge, Tyler, Jarrard, David F., LaViolette, Peter S.
Formato: Artigo
Idioma:Inglês
Publicado em: Society of Photo-Optical Instrumentation Engineers 2020
Assuntos:
Digital Pathology
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7550797/
https://ncbi.nlm.nih.gov/pubmed/33062803
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1117/1.JMI.7.5.057501
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