Deep convolutional neural network based HEp-2 cell classification

Xi Jia; Linlin Shen; Xiande Zhou; Shiqi Yu

doi:10.1109/ICPR.2016.7899611

Deep convolutional neural network based HEp-2 cell classification

Xi Jia
, Linlin Shen
, Xiande Zhou
, Shiqi Yu

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

34 Citations (Scopus)

Abstract

As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification methods, we proposed a network with deeper architecture. A class-balanced approach is also proposed to augment the HEp-2 cell dataset for network training. The proposed framework achieves an average class accuracy of 79.29% on ICPR 2012 HEp-2 dataset and a mean class accuracy of 98.26% on ICPR 2016 HEp-2 training set.

Original language	English
Title of host publication	2016 23rd International Conference on Pattern Recognition, ICPR 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	77-80
Number of pages	4
ISBN (Electronic)	9781509048472
DOIs	https://doi.org/10.1109/ICPR.2016.7899611
Publication status	Published - 1 Jan 2016
Externally published	Yes
Event	23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico Duration: 4 Dec 2016 → 8 Dec 2016

Publication series

Name	Proceedings - International Conference on Pattern Recognition
Volume	0
ISSN (Print)	1051-4651

Conference

Conference	23rd International Conference on Pattern Recognition, ICPR 2016
Country/Territory	Mexico
City	Cancun
Period	4/12/16 → 8/12/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Free Keywords

CNN
Class-balanced
Classification
Hep-2

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICPR.2016.7899611

Cite this

Jia, X., Shen, L., Zhou, X., & Yu, S. (2016). Deep convolutional neural network based HEp-2 cell classification. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (pp. 77-80). Article 7899611 (Proceedings - International Conference on Pattern Recognition; Vol. 0). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2016.7899611

@inproceedings{790e9f24329f4831b10414a7bd7f4a71,

title = "Deep convolutional neural network based HEp-2 cell classification",

abstract = "As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification methods, we proposed a network with deeper architecture. A class-balanced approach is also proposed to augment the HEp-2 cell dataset for network training. The proposed framework achieves an average class accuracy of 79.29\% on ICPR 2012 HEp-2 dataset and a mean class accuracy of 98.26\% on ICPR 2016 HEp-2 training set.",

keywords = "CNN, Class-balanced, Classification, Hep-2",

author = "Xi Jia and Linlin Shen and Xiande Zhou and Shiqi Yu",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd International Conference on Pattern Recognition, ICPR 2016 ; Conference date: 04-12-2016 Through 08-12-2016",

year = "2016",

month = jan,

day = "1",

doi = "10.1109/ICPR.2016.7899611",

language = "English",

series = "Proceedings - International Conference on Pattern Recognition",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "77--80",

booktitle = "2016 23rd International Conference on Pattern Recognition, ICPR 2016",

address = "United States",

}

Jia, X, Shen, L, Zhou, X & Yu, S 2016, Deep convolutional neural network based HEp-2 cell classification. in 2016 23rd International Conference on Pattern Recognition, ICPR 2016., 7899611, Proceedings - International Conference on Pattern Recognition, vol. 0, Institute of Electrical and Electronics Engineers Inc., pp. 77-80, 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 4/12/16. https://doi.org/10.1109/ICPR.2016.7899611

Deep convolutional neural network based HEp-2 cell classification. / Jia, Xi; Shen, Linlin; Zhou, Xiande et al.
2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 77-80 7899611 (Proceedings - International Conference on Pattern Recognition; Vol. 0).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep convolutional neural network based HEp-2 cell classification

AU - Jia, Xi

AU - Shen, Linlin

AU - Zhou, Xiande

AU - Yu, Shiqi

PY - 2016/1/1

Y1 - 2016/1/1

N2 - As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification methods, we proposed a network with deeper architecture. A class-balanced approach is also proposed to augment the HEp-2 cell dataset for network training. The proposed framework achieves an average class accuracy of 79.29% on ICPR 2012 HEp-2 dataset and a mean class accuracy of 98.26% on ICPR 2016 HEp-2 training set.

AB - As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification methods, we proposed a network with deeper architecture. A class-balanced approach is also proposed to augment the HEp-2 cell dataset for network training. The proposed framework achieves an average class accuracy of 79.29% on ICPR 2012 HEp-2 dataset and a mean class accuracy of 98.26% on ICPR 2016 HEp-2 training set.

KW - CNN

KW - Class-balanced

KW - Classification

KW - Hep-2

UR - https://www.scopus.com/pages/publications/85019169344

U2 - 10.1109/ICPR.2016.7899611

DO - 10.1109/ICPR.2016.7899611

M3 - Conference contribution

AN - SCOPUS:85019169344

T3 - Proceedings - International Conference on Pattern Recognition

SP - 77

EP - 80

BT - 2016 23rd International Conference on Pattern Recognition, ICPR 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 23rd International Conference on Pattern Recognition, ICPR 2016

Y2 - 4 December 2016 through 8 December 2016

ER -

Deep convolutional neural network based HEp-2 cell classification

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

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