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Proceedings Paper

Automatic labeling of molecular biomarkers on a cell-by-cell basis in immunohistochemistry images using convolutional neural networks
Author(s): Fahime Sheikhzadeh; Anita Carraro; Jagoda Korbelik; Calum MacAulay; Martial Guillaud; Rabab K. Ward
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Paper Abstract

This paper addresses the problem of classifying cells expressing different biomarkers. A deep learning based method that can automatically localize and count the cells expressing each of the different biomarkers is proposed. To classify the cells, a Convolutional Neural Network (CNN) was employed. Images of Immunohistochemistry (IHC) stained slides that contain these cells were digitally scanned. The images were taken from digital scans of IHC stained cervical tissues, acquired for a clinical trial. More than 4,500 RGB images of cells were used to train the CNN. To evaluate our method, the cells were first manually labeled based on the expressing biomarkers. Then we performed the classification on 156 randomly selected images of cells that were not used in training the CNN. The accuracy of the classification was 92% in this preliminary data set. The results have shown that this method has a good potential in developing an automatic method for immunohistochemical analysis.

Paper Details

Date Published: 23 March 2016
PDF: 6 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910R (23 March 2016); doi: 10.1117/12.2217046
Show Author Affiliations
Fahime Sheikhzadeh, The Univ. of British Columbia (Canada)
BC Cancer Research Ctr. (Canada)
Anita Carraro, BC Cancer Research Ctr. (Canada)
Jagoda Korbelik, BC Cancer Research Ctr. (Canada)
Calum MacAulay, BC Cancer Research Ctr. (Canada)
Martial Guillaud, BC Cancer Research Ctr. (Canada)
Rabab K. Ward, The Univ. of British Columbia (Canada)


Published in SPIE Proceedings Vol. 9791:
Medical Imaging 2016: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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