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International Conference on Information Technologies in Biomedicine

ITIB 2019: Information Technology in Biomedicine pp 222–233Cite as

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

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1011))

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.

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Acknowledgment

We thank the funds supported by the “National Natural Science Foundation of China” (No. 61806047), the “Fundamental Research Funds for the Central Universities” (No. N171903004), and the “Scientific Research Launched Fund of Liaoning Shihua University” (No. 2017XJJ-061). We also thank Dan Xue, due to her contribution is considered as the same important as the first author in this paper.

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Authors and Affiliations

  1. Microscopic Image and Medical Image Analysis Group, Northeastern University, Shenyang, China

    Chen Li, Dan Xue, Zhijie Hu, Hao Chen & Yudong Yao

  2. Liaoning Cancer Hospital & Institute, Shenyang, China

    Yong Zhang, Mo Li & Qian Wang

  3. Liaoning Shihua University, Fushun, China

    Ning Xu

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  1. Chen Li
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  2. Dan Xue
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  3. Zhijie Hu
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  4. Hao Chen
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  7. Mo Li
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Correspondence to Ning Xu .

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Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland

    Wojciech Wieclawek

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Li, C. et al. (2019). A Survey for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2019. Advances in Intelligent Systems and Computing, vol 1011. Springer, Cham. https://doi.org/10.1007/978-3-030-23762-2_20

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