Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

Gheisari, S; Catchpoole, D; Charlton, A; Kennedy, P

Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images

Gheisari, S Catchpoole, D Charlton, A Kennedy, P

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Publication Type:: Journal Article
Citation:: Journal of Pathology Informatics, 2018, 9 (1)
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Gheisari, S	en_US
dc.contributor.author	Catchpoole, D	en_US
dc.contributor.author	Charlton, A	en_US
dc.contributor.author	Kennedy, P https://orcid.org/0000-0001-7837-3171	en_US
dc.date.available	2018-04-11	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Journal of Pathology Informatics, 2018, 9 (1)	en_US
dc.identifier.issn	2229-5089	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131203
dc.description.abstract	© 2018 Journal of Pathology Informatics. Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.	en_US
dc.relation.ispartof	Journal of Pathology Informatics	en_US
dc.relation.isbasedon	10.4103/jpi.jpi_73_17	en_US
dc.title	Convolutional deep belief network with feature encoding for classification of neuroblastoma histological images	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	9	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0601 Biochemistry and Cell Biology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

© 2018 Journal of Pathology Informatics. Background: Neuroblastoma is the most common extracranial solid tumor in children younger than 5 years old. Optimal management of neuroblastic tumors depends on many factors including histopathological classification. The gold standard for classification of neuroblastoma histological images is visual microscopic assessment. In this study, we propose and evaluate a deep learning approach to classify high-resolution digital images of neuroblastoma histology into five different classes determined by the Shimada classification. Subjects and Methods: We apply a combination of convolutional deep belief network (CDBN) with feature encoding algorithm that automatically classifies digital images of neuroblastoma histology into five different classes. We design a three-layer CDBN to extract high-level features from neuroblastoma histological images and combine with a feature encoding model to extract features that are highly discriminative in the classification task. The extracted features are classified into five different classes using a support vector machine classifier. Data: We constructed a dataset of 1043 neuroblastoma histological images derived from Aperio scanner from 125 patients representing different classes of neuroblastoma tumors. Results: The weighted average F-measure of 86.01% was obtained from the selected high-level features, outperforming state-of-the-art methods. Conclusion: The proposed computer-aided classification system, which uses the combination of deep architecture and feature encoding to learn high-level features, is highly effective in the classification of neuroblastoma histological images.

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http://hdl.handle.net/10453/131203