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10 March 2020 Neural-network based classification of non-adherent cancer cells using Label free Quantitative Phase Imaging data (Conference Presentation)
Silvia Ceballos, Han Sang Park, Will J. Eldridge, Adam P. Wax
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Proceedings Volume 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020; 112511R (2020) https://doi.org/10.1117/12.2546281
Event: SPIE BiOS, 2020, San Francisco, California, United States
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.
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Silvia Ceballos, Han Sang Park, Will J. Eldridge, and Adam P. Wax "Neural-network based classification of non-adherent cancer cells using Label free Quantitative Phase Imaging data (Conference Presentation)", Proc. SPIE 11251, Label-free Biomedical Imaging and Sensing (LBIS) 2020, 112511R (10 March 2020); https://doi.org/10.1117/12.2546281
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KEYWORDS
Cancer
Phase imaging
Diagnostics
Digital holography
Holography
Microscopy
Optical microscopy
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