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Proceedings Paper

Digital pathology annotation data for improved deep neural network classification
Author(s): Edward Kim; Sai Lakshmi Deepika Mente; Andrew Keenan; Vijay Gehlot
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Paper Abstract

In the field of digital pathology, there is an explosive amount of imaging data being generated. Thus, there is an ever growing need to create assistive or automatic methods to analyze collections of images for screening and classification. Machine learning, specifically deep learning algorithms, developed for digital pathology have the potential to assist in this way. Deep learning architectures have demonstrated great success over existing classification models but require massive amounts of labeled training data that either doesn’t exist or are cost and time prohibitive to obtain. In this project, we present a framework for representing, collecting, validating, and utilizing cytopathology features for improved neural network classification.

Paper Details

Date Published: 13 March 2017
PDF: 7 pages
Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380D (13 March 2017); doi: 10.1117/12.2254491
Show Author Affiliations
Edward Kim, Villanova Univ. (United States)
Sai Lakshmi Deepika Mente, Villanova Univ. (United States)
Andrew Keenan, Villanova Univ. (United States)
Vijay Gehlot, Villanova Univ. (United States)

Published in SPIE Proceedings Vol. 10138:
Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
Tessa S. Cook; Jianguo Zhang, Editor(s)

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