File:Histopathology of tumor identification by humans and AI.png

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Histopathology with identification of breast cancer by humans and AI in whole-slide images

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Description
English: Histopathology with identification of breast cancer by humans and AI in whole-slide images. H&E stains. Original caption:
(A–C) Example whole-slide images from test TCGA data cohort with ground truth annotations from pathologists, (D–F) the corresponding region predictions produced by the ConvNet classifier and (G–I) region predictions for whole-slide images from the test NC data cohort of normal breast tissue without cancer.
Date
Source Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC (2017). "Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.". Sci Rep 7: 46450. DOI:10.1038/srep46450. PMID 28418027. PMC: 5394452.
"This work is licensed under a Creative Commons Attribution 4.0 International License."
Author Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC
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Date/TimeThumbnailDimensionsUserComment
current22:00, 1 January 2024Thumbnail for version as of 22:00, 1 January 20242,100 × 1,604 (3.93 MB)Mikael Häggström (talk | contribs)Uploaded a work by Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC from {{cite journal| author=Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC | display-authors=etal| title=Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. | journal=Sci Rep | year= 2017 | volume= 7 | issue= | pages= 46450 | pmid=28418027 | doi=10.1038/srep46450 | pmc=5394452 | url=https://www.ncb...

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