Journal
NATURE COMMUNICATIONS
Volume 8, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-017-00623-3
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Funding
- Helmholtz Postdoc Programme, Initiative and Networking Fund of the Helmholtz Association
- Biotechnology and Biological Sciences Research Council/National Science Foundation [BB/N005163/1]
- NSF DBI [1458626]
- ISAC EL programme
- Div Of Biological Infrastructure
- Direct For Biological Sciences [1458626] Funding Source: National Science Foundation
- BBSRC [BB/N005163/1] Funding Source: UKRI
- EPSRC [EP/J00619X/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/N005163/1] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/J00619X/1] Funding Source: researchfish
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We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.
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