{"id":291,"date":"2020-05-18T14:53:07","date_gmt":"2020-05-18T14:53:07","guid":{"rendered":"https:\/\/aimonks.com\/aibytes\/?p=291"},"modified":"2020-05-18T14:53:11","modified_gmt":"2020-05-18T14:53:11","slug":"intel-labs-upenn-use-federated-learning-for-early-brain-tumor-detection","status":"publish","type":"post","link":"https:\/\/aimonks.com\/aibytes\/2020\/05\/18\/intel-labs-upenn-use-federated-learning-for-early-brain-tumor-detection\/","title":{"rendered":"Intel Labs, UPenn Use Federated Learning For Early Brain Tumor Detection"},"content":{"rendered":"<span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">2<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span>\n<p>Intel Labs and the Perelman School of Medicine at the University of Pennsylvania (UPenn) have come together to develop a technology to train AI models to <strong>identify brain tumors while prioritizing preserving privacy<\/strong>. The technique using a distributed ML approach is called<strong> federated learning<\/strong>, which enables organizations to collaborate on deep learning projects without disclosing patient data.<\/p>\n\n\n\n<p>The Information Technology for Cancer Research (ITCR) program of the National Cancer Institute (NCI) is funding Penn Medicine&#8217;s work through a three-year, <strong>$1.2 million<\/strong> grant.<\/p>\n\n\n\n<p>Intel labs and Penn Medicine claimed to be the first to publish a paper on medical imaging federated learning.<\/p>\n\n\n\n<p>The companies<strong> first published<\/strong> the research at the International Conference of Medical Image Computing and Computer Assistant Intervention (MICCAI) 2018. The model trained with a federated learning method was over 99% accurate to a model trained under traditional, non-private methods.<\/p>\n\n\n\n<p>Both companies claim the work built on this new method will leverage Intel hardware and software to provide impenetrable privacy protection to both the model and the data.<\/p>\n\n\n\n<p>The two companies are coordinating with<strong> 29 healthcare and research institutions belonging to seven countries<\/strong>.<\/p>\n\n\n\n<p><strong>Jason Martin<\/strong>, principal engineer, Intel Labs, said, &#8220;AI could be helpful in early detection of brain tumor, but will require more data to reach its full potential than any single medical center holds.&#8221;<\/p>\n\n\n\n<p>AI has always been a significant contributor to the Healthcare industry. Babylon Health believes that it can appropriately triage patients in 85% cases, and Microsoft disclosed details of a $40 million worth <strong>&#8220;AI for Health&#8221; <\/strong>project.<\/p>\n\n\n\n<p><a href=\"https:\/\/artificialintelligence-news.com\/2020\/05\/11\/intel-and-upenn-utilising-federated-learning-to-identify-brain-tumours\/\">Source <\/a><\/p>\n\n\n\n<p>#AIMonks #IntelLabs #UPenn #ArtificialIntelligence #AI #ITCR #NCI # ML # MachineLearning #DeepLearning #Healthcare #FederatedLearning #Research<\/p>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">2<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span> Intel Labs and the Perelman School of Medicine at the University of Pennsylvania (UPenn) have come together to develop a technology to train AI models to identify brain tumors while prioritizing preserving privacy. The technique using a distributed ML approach is called federated learning, which enables organizations to collaborate on [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":292,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[51],"tags":[35,24,25,43,103,102,98,100,32,33,101,65,99],"class_list":["post-291","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovation","tag-ai","tag-ai-monks","tag-artificial-intelligence","tag-deep-learning","tag-federated-learning","tag-healthcare","tag-intel-labs","tag-itcr","tag-machine-learning","tag-ml","tag-nci","tag-research","tag-upenn"],"rttpg_featured_image_url":{"full":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/05\/computer-tomography-62942_640.jpg",640,456,false],"landscape":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/05\/computer-tomography-62942_640.jpg",640,456,false],"portraits":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/05\/computer-tomography-62942_640.jpg",640,456,false],"thumbnail":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/05\/computer-tomography-62942_640-150x150.jpg",150,150,true],"medium":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/05\/computer-tomography-62942_640-300x214.jpg",300,214,true],"large":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/05\/computer-tomography-62942_640.jpg",640,456,false],"1536x1536":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/05\/computer-tomography-62942_640.jpg",640,456,false],"2048x2048":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/05\/computer-tomography-62942_640.jpg",640,456,false],"hestia-blog":["https:\/\/aimonks.com\/aibytes\/wp-content\/uploads\/2020\/05\/computer-tomography-62942_640-360x240.jpg",360,240,true]},"rttpg_author":{"display_name":"AI Bytes","author_link":"https:\/\/aimonks.com\/aibytes\/author\/aibytes_kashika\/"},"rttpg_comment":0,"rttpg_category":"<a href=\"https:\/\/aimonks.com\/aibytes\/category\/daily-bytes\/innovation\/\" rel=\"category tag\">Innovation<\/a>","rttpg_excerpt":"Reading Time: 2 minutes Intel Labs and the Perelman School of Medicine at the University of Pennsylvania (UPenn) have come together to develop a technology to train AI models to identify brain tumors while prioritizing preserving privacy. The technique using a distributed ML approach is called federated learning, which enables organizations to collaborate on [&hellip;]","_links":{"self":[{"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/posts\/291","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/comments?post=291"}],"version-history":[{"count":1,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/posts\/291\/revisions"}],"predecessor-version":[{"id":293,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/posts\/291\/revisions\/293"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/media\/292"}],"wp:attachment":[{"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/media?parent=291"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/categories?post=291"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aimonks.com\/aibytes\/wp-json\/wp\/v2\/tags?post=291"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}