Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Doan, M, Vorobjev, I, Rees, P, Filby, A, Wolkenhauer, O, Goldfeld, AE, Lieberman, J, Barteneva, N, Carpenter, AE, Hennig, H |
Journal | Trends Biotechnol |
Volume | 36 |
Issue | 7 |
Pages | 649-652 |
Date Published | 2018 07 |
ISSN | 1879-3096 |
Keywords | Data Analysis, Deep Learning, Flow Cytometry, Humans, Image Processing, Computer-Assisted, Leukemia, Myeloid, Microscopy, Fluorescence, Neoplastic Cells, Circulating, Precision Medicine, Prognosis, Single Molecule Imaging, Single-Cell Analysis |
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. |
DOI | 10.1016/j.tibtech.2017.12.008 |
Pubmed | |
Alternate Journal | Trends Biotechnol. |
PubMed ID | 29395345 |
Grant List | BB/N005163 / / Biotechnology and Biological Sciences Research Council / United Kingdom |
Trends Biotechnol DOI:10.1016/j.tibtech.2017.12.008
Diagnostic Potential of Imaging Flow Cytometry.
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