Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning is a research paper published in Nature Biotechnology (2021). On theSindex it has a DataRank of 0. It has been cited 925 times.
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NIA NIH HHS
Grant: R01 AG057915
NCI NIH HHS
Grant: F31 CA246880
NIA NIH HHS
Grant: R01 AG056287
NIH HHS
Grant: DP5 OD019822
NCI NIH HHS
Grant: UH3 CA246633
NCI NIH HHS
Grant: P30 CA008748
National Institutes of Health
Grant: 5U24CA224309-03
Stanford Cancer Immune Monitoring and Analysis Center (CIMAC)
National Institutes of Health
Grant: 1R01AG057915-01
MIRIAD - Multiplexed Imaging of Resilience In Alzheimers Disease
National Institutes of Health
Grant: 5F31CA246880-02
Predicting response to anti-PD-1 therapy in triple negative breast cancer by comprehensive profiling of the tumor microenvironment
National Institutes of Health
Grant: 5R01AG056287-02
The Phenotypic Landscape of Cognitive Decline as Revealed by Next-Generation Multiplexed Ion Beam Imaging
Rita Allen Foundation
FWCI
119.15
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Spatial omics technologies at multimodal and single cell/subcellular level
Additional file 1 of Spatial omics technologies at multimodal and single cell/subcellular level
Additional file 1 of Cellstitch: 3D cellular anisotropic image segmentation via optimal transport
Additional file 1 of Cellstitch: 3D cellular anisotropic image segmentation via optimal transport
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