Chinese Optics Letters, Volume. 19, Issue 5, 051701(2021)

Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy

Ying Li1, Jianglei Di1、*, Li Ren2, and Jianlin Zhao1、**
Author Affiliations
  • 1MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions, and Shaanxi Key Laboratory of Optical Information Technology, School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710129, China
  • 2School of Life Sciences, Northwestern Polytechnical University, Xi’an 710072, China
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    References(29)

    [5] Ş. Öztürk, B. Akdemir. A convolutional neural network model for semantic segmentation of mitotic events in microscopy images. Neural Comput. Appl., 31, 3719(2018).

    [23] M. Beleggia, M. A. Schofield, V. V. Volkov, Y. Zhu. On the transport of intensity technique for phase retrieval. Ultmi, 102, 37(2004).

    [27] A. Krizhevsky, I. Sutskever, G. E. HintonInternational Conference on Neural Information Processing Systems. ImageNet classification with deep convolutional neural networks, 1097(2012).

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    Ying Li, Jianglei Di, Li Ren, Jianlin Zhao. Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy[J]. Chinese Optics Letters, 2021, 19(5): 051701

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    Paper Information

    Category: Biophotonics

    Received: Jul. 23, 2020

    Accepted: Nov. 26, 2020

    Published Online: Mar. 18, 2021

    The Author Email: Jianglei Di (jiangleidi@nwpu.edu.cn), Jianlin Zhao (jlzhao@nwpu.edu.cn)

    DOI:10.3788/COL202119.051701

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