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Proceedings Paper

Neural-network based classification of non-adherent cancer cells using Label free Quantitative Phase Imaging data (Conference Presentation)
Author(s): Silvia Ceballos; Han Sang Park; Will J. Eldridge; Adam P. Wax

Paper Abstract

We apply label-free imaging using digital holographic microscopy to analyze different cancer cell lines. Separation of cell lines based on extraction of amplitude and phase map variations along with post-processed, population specific parameters, was accomplished using machine learning. These data are used to train a neural network algorithm that attains accurate discrimination of non-adherent cancer cells.

Paper Details

Date Published: 10 March 2020
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Proc. SPIE 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020, 112511R (10 March 2020); doi: 10.1117/12.2546281
Show Author Affiliations
Silvia Ceballos, Duke Univ. (United States)
Han Sang Park, Duke Univ. (United States)
Will J. Eldridge, Duke Univ. (United States)
Adam P. Wax, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 11251:
Label-free Biomedical Imaging and Sensing (LBIS) 2020
Natan T. Shaked; Oliver Hayden, Editor(s)

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