期刊
SCIENTIFIC REPORTS
卷 7, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-017-07599-6
关键词
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资金
- 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)
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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