Drug Resistance Classification of Cancer Cells Based on Digital Holographic Flow Cytometry and Machine Learning

Lu Xin; Wen Xiao; Leiping Che; JinJin Liu; Lisa Miccio; Vittorio Bianco; Pasquale Memmolo; Pietro Ferraro; Xiaoping Li; Feng Pan

doi:10.1364/AIS.2021.JW1A.5

OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES)
OSA Technical Digest (Optica Publishing Group, 2021),
paper JW1A.5
•https://doi.org/10.1364/AIS.2021.JW1A.5

Drug Resistance Classification of Cancer Cells Based on Digital Holographic Flow Cytometry and Machine Learning

Lu Xin, Wen Xiao, Leiping Che, JinJin Liu, Lisa Miccio, Vittorio Bianco, Pasquale Memmolo, Pietro Ferraro, Xiaoping Li, and Feng Pan

Hyperspectral Imaging and Sounding of the Environment 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-90-3

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Joint Postdeadline Papers II (JW1A)

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L. Xin, W. Xiao, L. Che, J. Liu, L. Miccio, V. Bianco, P. Memmolo, P. Ferraro, X. Li, and F. Pan, "Drug Resistance Classification of Cancer Cells Based on Digital Holographic Flow Cytometry and Machine Learning," in OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES), S. Buckley, F. Vanier, S. Shi, K. Walker, I. Coddington, S. Paine, K. Lok Chan, W. Moses, S. Qian, P. Pellegrino, F. Vollmer, G. , J. Jágerská, R. Menzies, L. Emmenegger, and J. Westberg, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JW1A.5.

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Abstract

In this work, we use digital holographic (DH) microscope coupled to a label-free and high-throughput microfluidic cytometer to automatically detect the drug resistance of Epithelial Ovarian Cancer (EOC) cells reinforced by machine learning.

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