{"id":45799,"date":"2023-11-20T05:34:54","date_gmt":"2023-11-20T11:34:54","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/til-wsi-tcga-pub\/"},"modified":"2023-11-20T05:34:54","modified_gmt":"2023-11-20T11:34:54","slug":"til-wsi-tcga-pub","status":"publish","type":"tcia_citation","link":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/til-wsi-tcga-pub\/","title":{"rendered":"TIL-WSI-TCGA-PUB"},"template":"","class_list":["post-45799","tcia_citation","type-tcia_citation","status-publish"],"tcia_citation_type":"Publication Citation","tcia_citation_text":"<p><span>Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., Van Arnam, J., The Cancer Genome Atlas Research Network, Shmulevich, I., Rao, A. U. K., Lazar, A. J., Sharma, A., Thorsson, V. (2018). <strong>Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images<\/strong>. Cell Reports, 23(1), 181-193.e7. <a href=\"https:\/\/doi.org\/10.1016\/j.celrep.2018.03.086\">https:\/\/doi.org\/10.1016\/j.celrep.2018.03.086<\/a><\/span><\/p>\n","tcia_citation_statement":"","tcia_citation_doi":"10.1016\/j.celrep.2018.03.086","_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/citations\/45799","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/citations"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_citation"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=45799"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}