nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer is a research paper published in Cell Systems (2020). On theSindex it has a DataRank of 0. It has been cited 280 times.
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National Institutes of Health
Grant: MIRA R35 GM122547
Academy of Finland
Grant: 310552
Deep learning for phenotypic profiling of cancer cells
European Regional Development Fund
Grant: GINOP-2.3.2-15-2016-00026
Cancer Foundation Finland sr
Grant: 190113
NIGMS NIH HHS
Grant: R35 GM122547
National Institutes of Health
Grant: 3R35GM122547-02S1
Extracting rich information from biological images
National Institute of General Medical Sciences
Nvidia
FWCI
35.91
Citation Percentile
1.0%
Influential Citations
4
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of CellProfiler 4: improvements in speed, utility and usability
Additional file 1 of CellProfiler 4: improvements in speed, utility and usability
Additional file 1 of Optimizing deep learning-based segmentation of densely packed cells using cell surface markers
Additional file 1 of Optimizing deep learning-based segmentation of densely packed cells using cell surface markers
Pretrained nucleAIzer models for microscopy datasets
Pretrained nucleAIzer models for microscopy datasets
Pretrained nucleAIzer models for microscopy datasets