{"id":295,"date":"2020-04-24T10:00:06","date_gmt":"2020-04-24T09:00:06","guid":{"rendered":"https:\/\/journals.myesr.org\/insights-imaging\/2020\/04\/24\/deep-learning-workflow-in-radiology\/"},"modified":"2025-09-24T21:50:48","modified_gmt":"2025-09-24T20:50:48","slug":"deep-learning-workflow-in-radiology","status":"publish","type":"post","link":"https:\/\/journals.myesr.org\/insights-imaging\/highlights\/deep-learning-workflow-in-radiology\/","title":{"rendered":"Deep learning workflow in radiology"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-1346 aligncenter\" src=\"\/app\/uploads\/2020\/03\/Article-6.jpg\" alt=\"\" width=\"483\" height=\"429\" \/><br \/>\nThis article provides an overview of clinical use of deep learning, explains the creation of a multi-disciplinary team and summarizes current approaches to patient, data, model, and hardware selection. The authors also illustrate the workflow and discuss the challenges such as ethical considerations.<\/p>\n<p><strong> Article:<\/strong> <a href=\"https:\/\/insightsimaging.springeropen.com\/articles\/10.1186\/s13244-019-0832-5\" target=\"_blank\" rel=\"noopener noreferrer\">Deep learning workflow in radiology: a primer<\/a><\/p>\n<p><strong>Authors:<\/strong> Emmanuel Montagnon, Milena Cerny, Alexandre Cadrin-Ch\u00eanevert, Vincent Hamilton, Thomas Derennes, Andr\u00e9 Ilinca, Franck Vandenbroucke-Menu, Simon Turcotte, Samuel Kadoury and An Tang<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article provides an overview of clinical use of deep learning, explains the creation of a multi-disciplinary team and summarizes current approaches to patient, data, model, and hardware selection. The authors also illustrate the workflow and discuss the challenges such as ethical considerations. Article: Deep learning workflow in radiology: a primer Authors: Emmanuel Montagnon, Milena [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":14,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[],"class_list":["post-295","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-highlights"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Deep learning workflow in radiology - Insights into Imaging<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/journals.myesr.org\/insights-imaging\/highlights\/deep-learning-workflow-in-radiology\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep learning workflow in radiology - Insights into Imaging\" \/>\n<meta property=\"og:description\" content=\"This article provides an overview of clinical use of deep learning, explains the creation of a multi-disciplinary team and summarizes current approaches to patient, data, model, and hardware selection. 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