UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images o...
UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Upcoming technologies enable routine collection of highly multiplexed (20–60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a challenging problem commonly tackled with deep learning. In this paper, we report two findings that...
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UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues
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TN_cdi_doaj_primary_oai_doaj_org_article_d6d934461012487bae14696dab27c9da
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d6d934461012487bae14696dab27c9da
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ISSN
2399-3642
E-ISSN
2399-3642
DOI
10.1038/s42003-022-04076-3
