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Cervical Histopathology Image Classification Using Ensembled Transfer Learning

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Abstract

In recent years,Transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology inCervical Histopathology Image Classification (CHIC) becomes a new research domain. In this paper, we propose anEnsembled 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.

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Acknowledgements

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), the “Scientific Research Launched Fund of Liaoning Shihua University” (No. 2017XJJ-061), and the “Sichuan Science and Technology Program China” (No. 2018GZ0385). We also thank Dan Xue, due to her contribution is considered as the same 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 & Jinpeng Zhang

  2. Duke University, Durham, USA

    Fanjie Kong

  3. Shengjing Hospital, China Medical University, Shenyang, China

    Hongzan Sun & Le Zhang

  4. Chengdu University of Information Technology, Chendu, China

    Tao Jiang & Jianying Yuan

  5. Liaoning Shihua University, Fushun, China

    Ning Xu

Authors
  1. Chen Li

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  2. Dan Xue

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  3. Fanjie Kong

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  4. Zhijie Hu

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  5. Hao Chen

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  6. Yudong Yao

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  7. Hongzan Sun

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  8. Le Zhang

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  9. Jinpeng Zhang

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  10. Tao Jiang

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  11. Jianying Yuan

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  12. Ning Xu

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Corresponding author

Correspondence toNing Xu.

Editor information

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). Cervical Histopathology Image Classification Using Ensembled Transfer Learning. 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_3

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