Enhancements in localized classification for uterine cervical cancer digital histology image assessment

Enhancements in localized classification for uterine cervical cancer digital histology image assessment

Background: In previous research, we introduced an automated, localized, fusion-based approach for classifying uterine cervix squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on digitized histology image analysis. As part of the CIN asses...

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Bibliographic Details
Main Authors: Peng Guo, Haidar Almubarak, Koyel Banerjee, R Joe Stanley, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R Frazier, Randy H Moss, William V Stoecker
Format: Article
Language:English
Published: Elsevier 2016-01-01
Series:Journal of Pathology Informatics
Subjects:
Cervical cancer
cervical intraepithelial neoplasia
fusion-based classification
image processing
Online Access:http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2016;volume=7;issue=1;spage=51;epage=51;aulast=Guo

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