| 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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