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Peer-Reviewed Publication
Sci Rep2017;746450.April 18, 2017Journal Article

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.

Angel Cruz-Roa1,2, Hannah Gilmore3, Ajay Basavanhally4, Michael Feldman5, Shridar Ganesan6, Natalie N C Shih5, John Tomaszewski7, Fabio A González1, Anant Madabhushi8
1Universidad Nacional de Colombia, Bogota, Colombia.
2Universidad de los Llanos, Villavicencio, Colombia.
3University Hospitals Case Medical Center, Cleveland, OH, USA.
4Inspirata Inc., Tampa, FL, USA.
5Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
6Cancer Institute of New Jersey, New Brunswick, NJ, USA.
7University at Buffalo, The State University of New York, Buffalo, NY USA.
8Case Western Reserve University, Cleveland, OH, USA.
PubMed PMC DOI PDF

Abstract

With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessin…

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