Cervical Histopathology Image Classification Using Ensembled Transfer Learning

Chen Li; Dan Xue; Fanjie Kong; Zhijie Hu; Hao Chen; Yudong Yao; Hongzan Sun; Le Zhang; Jinpeng Zhang; Tao Jiang; Jianying Yuan; Ning Xu

doi:10.1007/978-3-030-23762-2_3

Cervical Histopathology Image Classification Using Ensembled Transfer Learning

Chen Li
, Dan Xue
, Fanjie Kong
, Zhijie Hu
, Hao Chen
, Yudong Yao
, Hongzan Sun
, Le Zhang
, Jinpeng Zhang
, Tao Jiang
, Jianying Yuan
, Ning Xu

Department of Electrical and Computer Engineering

Northeastern University China
Duke University
China Medical University
Chengdu University of Information Technology
Liaoning University of Petroleum and Chemical Technology

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

13 Scopus citations

Abstract

In recent years, Transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in Cervical Histopathology Image Classification (CHIC) becomes a new research domain. In this paper, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderately and poorly differentiated cervical histopathology images. In this ETL framework, Inception-V3 and VGG-16 based transfer learning structures are first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from these two structures. Finally, a late fusion based ensemble learning strategy is designed for the final classification. In the experiment, a practical dataset with 100 VEGF stained cervical histopathology images is applied to test the proposed ETL method in the CHIC task, and an average accuracy of 80% is achieved.

Original language	English
Title of host publication	Information Technology in Biomedicine, 2019
Editors	Ewa Pietka, Pawel Badura, Jacek Kawa, Wojciech Wieclawek
Pages	26-37
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-030-23762-2_3
State	Published - 2019
Event	7th International Conference on Information Technology in Biomedicine, ITIB 2019 - Kamień Śląski, Poland Duration: 18 Jun 2019 → 20 Jun 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1011
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	7th International Conference on Information Technology in Biomedicine, ITIB 2019
Country/Territory	Poland
City	Kamień Śląski
Period	18/06/19 → 20/06/19

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Cervical cancer
Classification
Deep learning
Differentiation stages
Ensemble learning
Histopathology image
Transfer learning

Access to Document

10.1007/978-3-030-23762-2_3

Cite this

Li, C., Xue, D., Kong, F., Hu, Z., Chen, H., Yao, Y., Sun, H., Zhang, L., Zhang, J., Jiang, T., Yuan, J., & Xu, N. (2019). Cervical Histopathology Image Classification Using Ensembled Transfer Learning. In E. Pietka, P. Badura, J. Kawa, & W. Wieclawek (Eds.), Information Technology in Biomedicine, 2019 (pp. 26-37). (Advances in Intelligent Systems and Computing; Vol. 1011). https://doi.org/10.1007/978-3-030-23762-2_3

@inproceedings{4aa76cb19a5c4f578f9d0579984bc50a,

title = "Cervical Histopathology Image Classification Using Ensembled Transfer Learning",

abstract = "In recent years, Transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in Cervical Histopathology Image Classification (CHIC) becomes a new research domain. In this paper, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderately and poorly differentiated cervical histopathology images. In this ETL framework, Inception-V3 and VGG-16 based transfer learning structures are first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from these two structures. Finally, a late fusion based ensemble learning strategy is designed for the final classification. In the experiment, a practical dataset with 100 VEGF stained cervical histopathology images is applied to test the proposed ETL method in the CHIC task, and an average accuracy of 80\% is achieved.",

keywords = "Cervical cancer, Classification, Deep learning, Differentiation stages, Ensemble learning, Histopathology image, Transfer learning",

author = "Chen Li and Dan Xue and Fanjie Kong and Zhijie Hu and Hao Chen and Yudong Yao and Hongzan Sun and Le Zhang and Jinpeng Zhang and Tao Jiang and Jianying Yuan and Ning Xu",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 7th International Conference on Information Technology in Biomedicine, ITIB 2019 ; Conference date: 18-06-2019 Through 20-06-2019",

year = "2019",

doi = "10.1007/978-3-030-23762-2\_3",

language = "English",

isbn = "9783030237615",

series = "Advances in Intelligent Systems and Computing",

pages = "26--37",

editor = "Ewa Pietka and Pawel Badura and Jacek Kawa and Wojciech Wieclawek",

booktitle = "Information Technology in Biomedicine, 2019",

}

Li, C, Xue, D, Kong, F, Hu, Z, Chen, H, Yao, Y, Sun, H, Zhang, L, Zhang, J, Jiang, T, Yuan, J & Xu, N 2019, Cervical Histopathology Image Classification Using Ensembled Transfer Learning. in E Pietka, P Badura, J Kawa & W Wieclawek (eds), Information Technology in Biomedicine, 2019. Advances in Intelligent Systems and Computing, vol. 1011, pp. 26-37, 7th International Conference on Information Technology in Biomedicine, ITIB 2019, Kamień Śląski, Poland, 18/06/19. https://doi.org/10.1007/978-3-030-23762-2_3

Cervical Histopathology Image Classification Using Ensembled Transfer Learning. / Li, Chen; Xue, Dan; Kong, Fanjie et al.
Information Technology in Biomedicine, 2019. ed. / Ewa Pietka; Pawel Badura; Jacek Kawa; Wojciech Wieclawek. 2019. p. 26-37 (Advances in Intelligent Systems and Computing; Vol. 1011).

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

TY - GEN

T1 - Cervical Histopathology Image Classification Using Ensembled Transfer Learning

AU - Li, Chen

AU - Xue, Dan

AU - Kong, Fanjie

AU - Hu, Zhijie

AU - Chen, Hao

AU - Yao, Yudong

AU - Sun, Hongzan

AU - Zhang, Le

AU - Zhang, Jinpeng

AU - Jiang, Tao

AU - Yuan, Jianying

AU - Xu, Ning

PY - 2019

Y1 - 2019

N2 - In recent years, Transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in Cervical Histopathology Image Classification (CHIC) becomes a new research domain. In this paper, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderately and poorly differentiated cervical histopathology images. In this ETL framework, Inception-V3 and VGG-16 based transfer learning structures are first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from these two structures. Finally, a late fusion based ensemble learning strategy is designed for the final classification. In the experiment, a practical dataset with 100 VEGF stained cervical histopathology images is applied to test the proposed ETL method in the CHIC task, and an average accuracy of 80% is achieved.

AB - In recent years, Transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in Cervical Histopathology Image Classification (CHIC) becomes a new research domain. In this paper, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderately and poorly differentiated cervical histopathology images. In this ETL framework, Inception-V3 and VGG-16 based transfer learning structures are first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from these two structures. Finally, a late fusion based ensemble learning strategy is designed for the final classification. In the experiment, a practical dataset with 100 VEGF stained cervical histopathology images is applied to test the proposed ETL method in the CHIC task, and an average accuracy of 80% is achieved.

KW - Cervical cancer

KW - Classification

KW - Deep learning

KW - Differentiation stages

KW - Ensemble learning

KW - Histopathology image

KW - Transfer learning

UR - https://www.scopus.com/pages/publications/85070760761

UR - https://www.scopus.com/pages/publications/85070760761#tab=citedBy

U2 - 10.1007/978-3-030-23762-2_3

DO - 10.1007/978-3-030-23762-2_3

M3 - Conference contribution

AN - SCOPUS:85070760761

SN - 9783030237615

T3 - Advances in Intelligent Systems and Computing

SP - 26

EP - 37

BT - Information Technology in Biomedicine, 2019

A2 - Pietka, Ewa

A2 - Badura, Pawel

A2 - Kawa, Jacek

A2 - Wieclawek, Wojciech

T2 - 7th International Conference on Information Technology in Biomedicine, ITIB 2019

Y2 - 18 June 2019 through 20 June 2019

ER -

Cervical Histopathology Image Classification Using Ensembled Transfer Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

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