Advanced Photonics, Volume. 2, Issue 2, 026001(2020)

Three-dimensional tomography of red blood cells using deep learning On the Cover

Joowon Lim*, Ahmed B. Ayoub, and Demetri Psaltis
Author Affiliations
  • École Polytechnique Fédérale de Lausanne, Optics Laboratory, Lausanne, Switzerland
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    References(36)

    [1] M. Born, E. Wolf. Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light(1999).

    [24] M. A. Yurkin et al. Discrete Dipole Simulations of Light Scattering by Blood Cells(2007).

    [29] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2014).

    [30] T. Salimans, D. P. Kingma. Weight normalization: a simple reparameterization to accelerate training of deep neural networks, 901-909(2016).

    [31] J. L. Ba, J. R. Kiros, G. E. Hinton. Layer normalization(2016).

    [34] I. Udroiu. Estimation of erythrocyte surface area in mammals(2014).

    CLP Journals

    [1] Chen Bai, Tong Peng, Junwei Min, Runze Li, Yuan Zhou, Baoli Yao, "Dual-wavelength in-line digital holography with untrained deep neural networks," Photonics Res. 9, 2501 (2021)

    [2] Dashan Dong, Kebin Shi, "Solving the missing cone problem by deep learning," Adv. Photon. 2, 020501 (2020)

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    Joowon Lim, Ahmed B. Ayoub, Demetri Psaltis, "Three-dimensional tomography of red blood cells using deep learning," Adv. Photon. 2, 026001 (2020)

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    Paper Information

    Category: Research Articles

    Received: Jan. 3, 2020

    Accepted: Mar. 4, 2020

    Published Online: Mar. 25, 2020

    The Author Email: Joowon Lim (limjoowon@gmail.com)

    DOI:10.1117/1.AP.2.2.026001

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