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Diagnostic Potential of Imaging Flow Cytometry

Minh Doan, Ivan Vorobjev, Paul Rees Orcid Logo, Andrew Filby, Olaf Wolkenhauer, Anne E. Goldfeld, Judy Lieberman, Natasha Barteneva, Anne E. Carpenter, Holger Hennig

Trends in Biotechnology

Swansea University Author: Paul Rees Orcid Logo

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DOI (Published version): 10.1016/j.tibtech.2017.12.008

Abstract

Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation...

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Published in: Trends in Biotechnology
ISSN: 0167-7799
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa38407
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first_indexed 2018-02-06T20:28:25Z
last_indexed 2018-03-19T20:35:34Z
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spelling 2018-03-19T16:22:55.8160942 v2 38407 2018-02-06 Diagnostic Potential of Imaging Flow Cytometry 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2018-02-06 MEDE Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice. Journal Article Trends in Biotechnology 0167-7799 deep learning; disease diagnostics; high-content analysis; imaging flow cytometry; translational medicine 31 12 2018 2018-12-31 10.1016/j.tibtech.2017.12.008 COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University 2018-03-19T16:22:55.8160942 2018-02-06T15:55:31.4435116 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Minh Doan 1 Ivan Vorobjev 2 Paul Rees 0000-0002-7715-6914 3 Andrew Filby 4 Olaf Wolkenhauer 5 Anne E. Goldfeld 6 Judy Lieberman 7 Natasha Barteneva 8 Anne E. Carpenter 9 Holger Hennig 10 0038407-06022018155702.pdf doan2018.pdf 2018-02-06T15:57:02.2200000 Output 997063 application/pdf Version of Record true 2018-02-06T00:00:00.0000000 true eng
title Diagnostic Potential of Imaging Flow Cytometry
spellingShingle Diagnostic Potential of Imaging Flow Cytometry
Paul Rees
title_short Diagnostic Potential of Imaging Flow Cytometry
title_full Diagnostic Potential of Imaging Flow Cytometry
title_fullStr Diagnostic Potential of Imaging Flow Cytometry
title_full_unstemmed Diagnostic Potential of Imaging Flow Cytometry
title_sort Diagnostic Potential of Imaging Flow Cytometry
author_id_str_mv 537a2fe031a796a3bde99679ee8c24f5
author_id_fullname_str_mv 537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
author Paul Rees
author2 Minh Doan
Ivan Vorobjev
Paul Rees
Andrew Filby
Olaf Wolkenhauer
Anne E. Goldfeld
Judy Lieberman
Natasha Barteneva
Anne E. Carpenter
Holger Hennig
format Journal article
container_title Trends in Biotechnology
publishDate 2018
institution Swansea University
issn 0167-7799
doi_str_mv 10.1016/j.tibtech.2017.12.008
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 1
active_str 0
description Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.
published_date 2018-12-31T03:48:34Z
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score 11.037603

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