{"id":1305,"date":"2019-01-14T10:54:32","date_gmt":"2019-01-14T15:54:32","guid":{"rendered":"https:\/\/mlmed.org\/w\/?p=1305"},"modified":"2020-07-18T15:50:15","modified_gmt":"2020-07-18T19:50:15","slug":"chester-a-web-delivered-locally-computed-chest-x-ray-disease-prediction-system","status":"publish","type":"post","link":"https:\/\/mlmed.org\/w\/chester-a-web-delivered-locally-computed-chest-x-ray-disease-prediction-system\/","title":{"rendered":"Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System"},"content":{"rendered":"<p>In order to bridge the gap between Deep Learning researchers and medical professionals we develop a very accessible free prototype system which can be used by medical professionals to understand the reality of Deep Learning tools for chest X-ray diagnostics. The system is designed to be a second opinion where a user can process an image to confirm or aid in their diagnosis. Code and network weights are delivered via a URL to a web browser (including cell phones) but the patient data remains on the users machine and all processing occurs locally. This paper discusses the three main components in detail: out-of-distribution detection, disease prediction, and prediction explanation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In order to bridge the gap between Deep Learning researchers and medical professionals we develop a very accessible free prototype system which can be used by medical professionals to understand the reality of Deep Learning tools for chest X-ray diagnostics. The system is designed to be a second opinion where a user can process an [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[22],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/posts\/1305"}],"collection":[{"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/comments?post=1305"}],"version-history":[{"count":3,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/posts\/1305\/revisions"}],"predecessor-version":[{"id":1447,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/posts\/1305\/revisions\/1447"}],"wp:attachment":[{"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/media?parent=1305"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/categories?post=1305"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mlmed.org\/w\/wp-json\/wp\/v2\/tags?post=1305"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}