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Lensfree On-chip Microscopy Achieves Accurate Measurement of Yeast Cell Viability and Concentration Using Machine Learning

Feizi, Alborz and Zhang, Yibo and Greenbaum, Alon and Guziak, Alex and Luong, Michelle and Chan, Raymond Yan Lok and Berg, Brandon and Ozkan, Haydar and Luo, Wei and Wu, Michael and Wu, Yichen and Ozcan, Aydogan (2017) Lensfree On-chip Microscopy Achieves Accurate Measurement of Yeast Cell Viability and Concentration Using Machine Learning. In: 2017 Conference on Lasers and Electro-Optics (CLEO). IEEE , Piscataway, NJ, ART. NO. ATh4B.4. ISBN 978-1-9435-8027-9. http://resolver.caltech.edu/CaltechAUTHORS:20180420-125357838

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Abstract

Automatic measurement of yeast viability and concentration is achieved by coupling a lensfree on-chip holographic microscope with a machine learning based classification algorithm that counts the number of live/dead cells stained with methylene blue.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1364/CLEO_AT.2017.ATh4B.4DOIArticle
https://www.osapublishing.org/abstract.cfm?URI=CLEO_AT-2017-ATh4B.4PublisherArticle
https://ieeexplore.ieee.org/document/8082952PublisherArticle
Additional Information:© 2017 IEEE.
Classification Code:OCIS codes: (090.1995) Digital holography; (110.1758) Computational imaging
Record Number:CaltechAUTHORS:20180420-125357838
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180420-125357838
Official Citation:A. Feizi et al., "Lensfree on-chip microscopy achieves accurate measurement of yeast cell viability and concentration using machine learning," 2017 Conference on Lasers and Electro-Optics (CLEO), San Jose, CA, 2017, pp. 1-2. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8082952&isnumber=8082845
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85989
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:25 Apr 2018 18:36
Last Modified:25 Apr 2018 18:36

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