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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Dettagli Bibliografici
Pubblicato in:J Med Imaging (Bellingham)
Autori principali: 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.
Natura: Artigo
Lingua:Inglês
Pubblicazione: Society of Photo-Optical Instrumentation Engineers 2020
Soggetti:
Digital Pathology
Accesso online: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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