{"id":41281,"date":"2023-11-20T00:53:21","date_gmt":"2023-11-20T06:53:21","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/ovarian-bevacizumab-response-pub\/"},"modified":"2023-11-20T00:53:21","modified_gmt":"2023-11-20T06:53:21","slug":"ovarian-bevacizumab-response-pub","status":"publish","type":"tcia_citation","link":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/ovarian-bevacizumab-response-pub\/","title":{"rendered":"OVARIAN-BEVACIZUMAB-RESPONSE-PUB"},"template":"","class_list":["post-41281","tcia_citation","type-tcia_citation","status-publish"],"tcia_citation_type":"Publication Citation","tcia_citation_text":"<p>Wang, C.-W., Chang, C.-C., Lee, Y.-C., Lin, Y.-J., Lo, S.-C., Hsu, P.-C., Liou, Y.-A., Wang, C.-H., &amp; Chao, T.-K. (2022). <strong>Weakly Supervised Deep Learning for Prediction of Treatment Effectiveness on Ovarian Cancer from Histopathology Images.<\/strong> In Computerized Medical Imaging and Graphics (p. 102093). Elsevier BV. <a href=\"https:\/\/doi.org\/10.1016\/j.compmedimag.2022.102093\">https:\/\/doi.org\/10.1016\/j.compmedimag.2022.102093<\/a><\/p>\n","tcia_citation_statement":"","tcia_citation_doi":"10.1016\/j.compmedimag.2022.102093","_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/citations\/41281","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=41281"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}