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Proceedings Paper

Multiplication free neural network for cancer stem cell detection in H-and-E stained liver images
Author(s): Diaa Badawi; Ece Akhan; Ma'en Mallah; Ayşegül Üner; Rengül Çetin-Atalay; A. Enis Çetin
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Paper Abstract

Markers such as CD13 and CD133 have been used to identify Cancer Stem Cells (CSC) in various tissue images. It is highly likely that CSC nuclei appear as brown in CD13 stained liver tissue images. We observe that there is a high correlation between the ratio of brown to blue colored nuclei in CD13 images and the ratio between the dark blue to blue colored nuclei in H&E stained liver images. Therefore, we recommend that a pathologist observing many dark blue nuclei in an H&E stained tissue image may also order CD13 staining to estimate the CSC ratio. In this paper, we describe a computer vision method based on a neural network estimating the ratio of dark blue to blue colored nuclei in an H&E stained liver tissue image. The neural network structure is based on a multiplication free operator using only additions and sign operations. Experimental results are presented.

Paper Details

Date Published: 5 May 2017
PDF: 9 pages
Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 102110C (5 May 2017); doi: 10.1117/12.2262338
Show Author Affiliations
Diaa Badawi, Bilkent Univ. (Turkey)
Ece Akhan, Middle East Technical Univ. (Turkey)
Ma'en Mallah, Bilkent Univ. (Turkey)
Ayşegül Üner, Hacettepe Univ. (Turkey)
Rengül Çetin-Atalay, Middle East Technical Univ. (Turkey)
A. Enis Çetin, Bilkent Univ. (Turkey)
Univ. of Illinois (United States)

Published in SPIE Proceedings Vol. 10211:
Compressive Sensing VI: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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