Drug resistance classification of cancer cells based on digital holographic flow cytometry and machine learning

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

Drug resistance classification of cancer cells based on digital holographic flow cytometry and machine learning

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

School of Instrumentation and Optoelectronic Engineering

Beihang University
Peking University
National Research Council of Italy

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Article number	JW1A.5
Journal	Optics InfoBase Conference Papers
State	Published - 2021
Event	Applied Industrial Spectroscopy, AIS 2021 - Part of Optical Sensors and Sensing Congress 2021 - Virtual, Online, United States Duration: 19 Jul 2021 → 23 Jul 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Cite this

@article{97130389da974a8daf8becc67ef1dadd,

title = "Drug resistance classification of cancer cells based on digital holographic flow cytometry and machine learning",

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.",

author = "Lu Xin and Wen Xiao and Leiping Che and Liu, \{Jin Jin\} and Lisa Miccio and Vittorio Bianco and Pasquale Memmolo and Pietro Ferraro and Xiaoping Li and Feng Pan",

note = "Publisher Copyright: {\textcopyright} OSA 2021, {\textcopyright} 2021 The Author(s); Applied Industrial Spectroscopy, AIS 2021 - Part of Optical Sensors and Sensing Congress 2021 ; Conference date: 19-07-2021 Through 23-07-2021",

year = "2021",

language = "英语",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

}

TY - JOUR

T1 - Drug resistance classification of cancer cells based on digital holographic flow cytometry and machine learning

AU - Xin, Lu

AU - Xiao, Wen

AU - Che, Leiping

AU - Liu, Jin Jin

AU - Miccio, Lisa

AU - Bianco, Vittorio

AU - Memmolo, Pasquale

AU - Ferraro, Pietro

AU - Li, Xiaoping

AU - Pan, Feng

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85119379394

M3 - 会议文章

AN - SCOPUS:85119379394

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - JW1A.5

T2 - Applied Industrial Spectroscopy, AIS 2021 - Part of Optical Sensors and Sensing Congress 2021

Y2 - 19 July 2021 through 23 July 2021

ER -

Drug resistance classification of cancer cells based on digital holographic flow cytometry and machine learning

Abstract

UN SDGs

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