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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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| Publicado en: | J Med Imaging (Bellingham) |
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| Main Authors: | , , , , , , , , , , , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
Society of Photo-Optical Instrumentation Engineers
2020
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| Assuntos: | |
| Acceso en liña: | 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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