Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity-Reference-Cited by-同舟云学术

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity

Published:2020 Issue:20 Volume:20 Page:3696-3708
ISSN:1473-0197
Container-title:Lab on a Chip
language:en
Short-container-title:Lab Chip

Author:

Siu Dickson M. D.¹²³⁴^ORCID,Lee Kelvin C. M.¹²³⁴^ORCID,Lo Michelle C. K.¹²³⁴^ORCID,Stassen Shobana V.¹²³⁴,Wang Maolin¹²³⁴,Zhang Iris Z. Q.¹²³⁴,So Hayden K. H.¹²³⁴^ORCID,Chan Godfrey C. F.⁵⁶³⁴^ORCID,Cheah Kathryn S. E.⁷⁶³⁴^ORCID,Wong Kenneth K. Y.¹²³⁴^ORCID,Hsin Michael K. Y.⁸⁹⁴,Ho James C. M.¹⁰⁶³⁴^ORCID,Tsia Kevin K.¹²³⁴^ORCID

Affiliation:

1. Department of Electrical and Electronic Engineering

2. Choi Yei Ching Building

3. The University of Hong Kong

4. Hong Kong

5. Department of Pediatrics and Adolescent Medicine

6. Li Ka Shing Faculty of Medicine

7. School of Biomedical Sciences

8. Department of Cardiothoracic Surgery

9. Queen Mary Hospital

10. Department of Medicine

Abstract

An optofluidic imaging cytometry strategy that hierarchically analyzes single-cell biophysical morphology beyond millions of cells at high sensitivity and specificity.

Funder

Research Grants Council, University Grants Committee

Innovation and Technology Commission - Hong Kong

University of Hong Kong

Publisher

Royal Society of Chemistry (RSC)

Subject

Biomedical Engineering,General Chemistry,Biochemistry,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2020/LC/D0LC00542H

Reference64 articles.

1. Data-analysis strategies for image-based cell profiling