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.
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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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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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DOI: https://doi.org/10.1007/978-3-030-23762-2_3
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