Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images

Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images

Context: Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise and relatively expensive in low-income settings. On...

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Bibliographic Details
Main Authors: Deepak Anand, Nikhil Cherian Kurian, Shubham Dhage, Neeraj Kumar, Swapnil Rane, Peter H Gann, Amit Sethi
Format: Article
Language:English
Published: Wolters Kluwer Medknow Publications 2020-01-01
Series:Journal of Pathology Informatics
Subjects:
breast cancer
convolutional neural networks
histopathology
human epidermal growth factor receptor 2
immunohistochemistry
mutation detection
nucleus detection
Online Access:http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=19;epage=19;aulast=Anand

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http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2020;volume=11;issue=1;spage=19;epage=19;aulast=Anand

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