Diagnostic Potential of Imaging Flow Cytometry. Issue 7 (July 2018)

• Record Type: Journal Article
• Authors: Doan, Minh; Vorobjev, Ivan; Rees, Paul; Filby, Andrew; Wolkenhauer, Olaf; Goldfeld, Anne E.; Lieberman, Judy; Barteneva, Natasha; Carpenter, Anne E.; Hennig, Holger
• Is Part Of: Trends in biotechnology. Volume 36:Issue 7(2018)
• Page Start: 649
• Page End: 652
• Publication Date: 2018-07
• Subjects: deep learning -- disease diagnostics -- high-content analysis -- imaging flow cytometry -- translational medicine; Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Genetic engineering -- Periodicals; Industrial microbiology -- Periodicals; 660.605
• DOI: 10.1016/j.tibtech.2017.12.008
• Languages: English
• ISSNs: 0167-7799

Abstract

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 into clinical practice.