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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Bibliografische gegevens
Gepubliceerd in:J Med Imaging (Bellingham)
Hoofdauteurs: 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.
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Society of Photo-Optical Instrumentation Engineers 2020
Onderwerpen:
Digital Pathology
Online toegang: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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