Diagnostic Potential of Imaging Flow Cytometry

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dc.contributor.authorMinh Doan
dc.contributor.authorIvan Vorobjev
dc.contributor.authorPaul Rees
dc.contributor.authorAndrew Filby
dc.contributor.authorOlaf Wolkenhauer
dc.contributor.authorAnne E. Goldfeld
dc.contributor.authorJudy Lieberman
dc.contributor.authorNatasha Barteneva
dc.contributor.authorAnne E. Carpenter
dc.contributor.authorHolger Hennig
dc.date.accessioned2025-08-06T11:54:07Z
dc.date.available2025-08-06T11:54:07Z
dc.date.issued2018
dc.description.abstractImaging 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...
dc.identifier.citationDoan, M. et al. (2018). Diagnostic Potential of Imaging Flow Cytometry. Trends in Biotechnology, 36(7), 649–652. DOI: 10.1016/j.tibtech.2017.12.008
dc.identifier.urihttps://nur.nu.edu.kz/handle/123456789/9129
dc.language.isoen
dc.subjectimaging flow cytometry
dc.subjectdeep learning
dc.subjecthigh-content analysis
dc.subjectdisease diagnostics
dc.subjecttranslational medicine
dc.titleDiagnostic Potential of Imaging Flow Cytometry
dc.typeArticle

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