{"id":41611,"date":"2023-11-20T01:40:25","date_gmt":"2023-11-20T07:40:25","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/sln-breast-pub\/"},"modified":"2023-11-20T01:40:25","modified_gmt":"2023-11-20T07:40:25","slug":"sln-breast-pub","status":"publish","type":"tcia_citation","link":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/sln-breast-pub\/","title":{"rendered":"SLN-BREAST-PUB"},"template":"","class_list":["post-41611","tcia_citation","type-tcia_citation","status-publish"],"tcia_citation_type":"Publication Citation","tcia_citation_text":"<p><span>Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., &amp; Fuchs, T. J. (2019). <strong>Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.<\/strong> Nature Medicine (Vol. 25, Issue 8, pp. 1301\u20131309). Springer Science and Business Media LLC. <a href=\"https:\/\/doi.org\/10.1038\/s41591-019-0508-1\">https:\/\/doi.org\/10.1038\/s41591-019-0508-1<\/a><\/span><\/p>\n","tcia_citation_statement":"","tcia_citation_doi":"10.1038\/s41591-019-0508-1","_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/citations\/41611","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=41611"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}