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Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images

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Sirinukunwattana, Korsuk, Raza, Shan-e-Ahmed, Tsang, Yee-Wah, Snead, David, Cree, Ian A. and Rajpoot, Nasir M. (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Transactions on Medical Imaging, 35 (5). 1196 -1206. doi:10.1109/TMI.2016.2525803 ISSN 0278-0062.

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Official URL: http://dx.doi.org/10.1109/TMI.2016.2525803

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Abstract

Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the center of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and could potentially lead to a better understanding of cancer.

Item Type: Journal Article
Subjects: R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH): Colon (Anatomy) -- Cancer, Histology -- Technique, Image analysis, Neural networks (Computer science)
Journal or Publication Title: IEEE Transactions on Medical Imaging
Publisher: IEEE
ISSN: 0278-0062
Official Date: May 2016
Dates:
DateEvent
May 2016Published
4 February 2016Available
2015Accepted
Volume: 35
Number: 5
Page Range: 1196 -1206
DOI: 10.1109/TMI.2016.2525803
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 26 February 2016
Date of first compliant Open Access: 29 February 2016
Funder: Qatar National Research Fund (QNRF), University of Warwick. Department of Computer Science
Grant number: NPRP5-1345-1-228 (QNRF)

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