{"id":1324,"date":"2021-03-30T15:49:42","date_gmt":"2021-03-30T13:49:42","guid":{"rendered":"http:\/\/iwann.uma.es\/2021\/?page_id=1324"},"modified":"2021-04-04T15:42:59","modified_gmt":"2021-04-04T13:42:59","slug":"abstracts12","status":"publish","type":"page","link":"http:\/\/iwann-old.uma.es\/2021\/?page_id=1324","title":{"rendered":"Abstract S12"},"content":{"rendered":"\t\t
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S12: Information fusion in Deep Learning for Biomedicine<\/h1>

Miguel Atencia<\/h2>

Francisco Veredas<\/h2>

Universidad de M\u00e1laga, Spain<\/h3>

Ruxandra Stoean<\/h2>

Romanian Institute of Science and Technology, Romania<\/h3>

University of Craiova, Romania<\/h3>
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Abstract<\/span><\/h3>
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The story goes that a picture is worth a thousand words and the success of Deep Learning techniques in image classification bears witness to the adage. Yet in many biomedical applications the availability of labeled images of diagnostic quality is rather limited as compared to, say, cats and dogs pictures. It comes as no surprise, then, that medical experts use a variety of media to diagnose, inform, and record conditions: images (magnetic resonance, tomography, ultrasound), signals (electrocardiogram, electroencephalogram), multi-omics data, and quite remarkably, natural language.<\/span><\/div>
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This special session aims at further exploring the application of Machine Learning methods to gain biomedical knowledge, emphasizing the integration of sources of information and highlighting the exploitation of data in scenarios where experience is scarce so far. The usage of word embeddings in natural language processing, echocardiography, fetal ultrasound, and analysis and prediction of COVID-19 data come to mind as examples, of course with no claim to be exhaustive. The scope of involved techniques is wide, including convolutional networks, statistical approaches, multi-layer perceptrons, and recurrent models.<\/p>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

S12: Information fusion in Deep Learning for Biomedicine Miguel Atencia Francisco Veredas Universidad de M\u00e1laga, Spain Ruxandra Stoean Romanian Institute of Science and Technology, Romania University of Craiova, Romania \u00a0 \u00a0 \u00a0 Abstract \u00a0The story goes that a picture is worth a thousand words and the success of Deep Learning techniques in image classification bears Read more about Abstract S12<\/span>[…]<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"http:\/\/iwann-old.uma.es\/2021\/index.php?rest_route=\/wp\/v2\/pages\/1324"}],"collection":[{"href":"http:\/\/iwann-old.uma.es\/2021\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/iwann-old.uma.es\/2021\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/iwann-old.uma.es\/2021\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/iwann-old.uma.es\/2021\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1324"}],"version-history":[{"count":26,"href":"http:\/\/iwann-old.uma.es\/2021\/index.php?rest_route=\/wp\/v2\/pages\/1324\/revisions"}],"predecessor-version":[{"id":1364,"href":"http:\/\/iwann-old.uma.es\/2021\/index.php?rest_route=\/wp\/v2\/pages\/1324\/revisions\/1364"}],"wp:attachment":[{"href":"http:\/\/iwann-old.uma.es\/2021\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1324"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}