Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

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ID: 120348
2018
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Abstract
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are deriv …
Reference Key
j2018cellspatial Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Shmulevich I, Rao Auk, Lazar AJ, Sharma A, Thorsson V
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Journal Cell reports
Year 2018
DOI
DOI not found
URL
https://www.pubmed.gov/29617659/
Keywords
National Center for Biotechnology Information NCBI NLM MEDLINE humans pubmed abstract nih national institutes of health national library of medicine research support non-u.s. gov't u.s. gov't non-p.h.s. N.I.H. Extramural computer-assisted / methods* image interpretation neoplasms / pathology* pmid:29617659 pmc5943714 doi:10.1016/j.celrep.2018.03.086 joel saltz rajarsi gupta vésteinn thorsson deep learning* lymphocytes tumor-infiltrating / metabolism tumor-infiltrating / pathology*

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