{"id":41539,"date":"2023-11-20T01:38:05","date_gmt":"2023-11-20T07:38:05","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/breast-cancer-screening-dbt-pub\/"},"modified":"2023-11-20T01:38:05","modified_gmt":"2023-11-20T07:38:05","slug":"breast-cancer-screening-dbt-pub","status":"publish","type":"tcia_citation","link":"https:\/\/stage.cancerimagingarchive.net\/tcia-citation\/breast-cancer-screening-dbt-pub\/","title":{"rendered":"BREAST-CANCER-SCREENING-DBT-PUB"},"template":"","class_list":["post-41539","tcia_citation","type-tcia_citation","status-publish"],"tcia_citation_type":"Publication Citation","tcia_citation_text":"<p><span><span>Buda, M., Saha, A., Walsh, R., Ghate, S., Li, N., Swiecicki, A., Lo, J. Y., &amp; Mazurowski, M. A. (2021). A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. In JAMA Network Open (Vol. 4, Issue 8, p. e2119100). American Medical Association (AMA).<\/span> <a href=\"https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100\">https:\/\/doi.org\/10.1001\/jamanetworkopen.2021.19100<\/a>, PMC8369362<\/span><\/p>\n","tcia_citation_statement":"","tcia_citation_doi":"10.1001\/jamanetworkopen.2021.19100","_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/citations\/41539","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=41539"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}