Journal
SCIENTIFIC REPORTS
Volume 7, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-017-07599-6
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Funding
- Swedish research council [2012-4968]
- ERC Consolidator grant [682810]
- Swedish strategic research program, eSSENCE
- European Research Council (ERC) [682810] Funding Source: European Research Council (ERC)
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Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations.
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