A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Chen Li; Dan Xue; Zhijie Hu; Hao Chen; Yudong Yao; Yong Zhang; Mo Li; Qian Wang; Ning Xu

doi:10.1007/978-3-030-23762-2_20

A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Chen Li, Dan Xue, Zhijie Hu, Hao Chen, Yudong Yao, Yong Zhang, Mo Li, Qian Wang, Ning Xu

Department of Electrical and Computer Engineering

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

17 Scopus citations

Abstract

Because Breast Histopathology Image Analysis (BHIA) plays a very important role in breast cancer diagnosis and medical treatment processes, more and more effective Machine Learning (ML) techniques are developed and applied in this field to assist histopathologists to obtain a more rapid, stable, objective, and quantified analysis result. Among all the applied ML algorithms in the BHIA field, Artificial Neural Networks (ANNs) show a very positive and healthy development trend in recent years. Hence, in order to clarify the development history and find the future potential of ANNs in the BHIA field, we survey more than 60 related works in this paper, referring to classical ANNs, deep ANNs and methodology analysis.

Original language	English
Title of host publication	Information Technology in Biomedicine, 2019
Editors	Ewa Pietka, Pawel Badura, Jacek Kawa, Wojciech Wieclawek
Pages	222-233
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-030-23762-2_20
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

Keywords

Artificial neural networks
Breast cancer
Classification
Deep learning
Feature extraction
Histopathology image

UN SDGs

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

Access to Document

10.1007/978-3-030-23762-2_20

Cite this

Li, C., Xue, D., Hu, Z., Chen, H., Yao, Y., Zhang, Y., Li, M., Wang, Q., & Xu, N. (2019). A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks. In E. Pietka, P. Badura, J. Kawa, & W. Wieclawek (Eds.), Information Technology in Biomedicine, 2019 (pp. 222-233). (Advances in Intelligent Systems and Computing; Vol. 1011). https://doi.org/10.1007/978-3-030-23762-2_20

@inproceedings{8205d37d51f047dc92ea8eb707332ab3,

title = "A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks",

abstract = "Because Breast Histopathology Image Analysis (BHIA) plays a very important role in breast cancer diagnosis and medical treatment processes, more and more effective Machine Learning (ML) techniques are developed and applied in this field to assist histopathologists to obtain a more rapid, stable, objective, and quantified analysis result. Among all the applied ML algorithms in the BHIA field, Artificial Neural Networks (ANNs) show a very positive and healthy development trend in recent years. Hence, in order to clarify the development history and find the future potential of ANNs in the BHIA field, we survey more than 60 related works in this paper, referring to classical ANNs, deep ANNs and methodology analysis.",

keywords = "Artificial neural networks, Breast cancer, Classification, Deep learning, Feature extraction, Histopathology image",

author = "Chen Li and Dan Xue and Zhijie Hu and Hao Chen and Yudong Yao and Yong Zhang and Mo Li and Qian Wang 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_20",

language = "English",

isbn = "9783030237615",

series = "Advances in Intelligent Systems and Computing",

pages = "222--233",

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

booktitle = "Information Technology in Biomedicine, 2019",

}

Li, C, Xue, D, Hu, Z, Chen, H, Yao, Y, Zhang, Y, Li, M, Wang, Q & Xu, N 2019, A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks. in E Pietka, P Badura, J Kawa & W Wieclawek (eds), Information Technology in Biomedicine, 2019. Advances in Intelligent Systems and Computing, vol. 1011, pp. 222-233, 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_20

A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks. / Li, Chen; Xue, Dan; Hu, Zhijie et al.
Information Technology in Biomedicine, 2019. ed. / Ewa Pietka; Pawel Badura; Jacek Kawa; Wojciech Wieclawek. 2019. p. 222-233 (Advances in Intelligent Systems and Computing; Vol. 1011).

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

TY - GEN

T1 - A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

AU - Li, Chen

AU - Xue, Dan

AU - Hu, Zhijie

AU - Chen, Hao

AU - Yao, Yudong

AU - Zhang, Yong

AU - Li, Mo

AU - Wang, Qian

AU - Xu, Ning

PY - 2019

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N2 - Because Breast Histopathology Image Analysis (BHIA) plays a very important role in breast cancer diagnosis and medical treatment processes, more and more effective Machine Learning (ML) techniques are developed and applied in this field to assist histopathologists to obtain a more rapid, stable, objective, and quantified analysis result. Among all the applied ML algorithms in the BHIA field, Artificial Neural Networks (ANNs) show a very positive and healthy development trend in recent years. Hence, in order to clarify the development history and find the future potential of ANNs in the BHIA field, we survey more than 60 related works in this paper, referring to classical ANNs, deep ANNs and methodology analysis.

AB - Because Breast Histopathology Image Analysis (BHIA) plays a very important role in breast cancer diagnosis and medical treatment processes, more and more effective Machine Learning (ML) techniques are developed and applied in this field to assist histopathologists to obtain a more rapid, stable, objective, and quantified analysis result. Among all the applied ML algorithms in the BHIA field, Artificial Neural Networks (ANNs) show a very positive and healthy development trend in recent years. Hence, in order to clarify the development history and find the future potential of ANNs in the BHIA field, we survey more than 60 related works in this paper, referring to classical ANNs, deep ANNs and methodology analysis.

KW - Artificial neural networks

KW - Breast cancer

KW - Classification

KW - Deep learning

KW - Feature extraction

KW - Histopathology image

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ER -

A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Abstract

Publication series

Conference

Keywords

UN SDGs

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