{"id":1451,"date":"2020-03-21T10:58:32","date_gmt":"2020-03-21T14:58:32","guid":{"rendered":"http:\/\/josephpcohen.com\/w\/?p=1451"},"modified":"2022-04-10T21:55:45","modified_gmt":"2022-04-11T01:55:45","slug":"public-covid19-dataset","status":"publish","type":"post","link":"https:\/\/josephpcohen.com\/w\/public-covid19-dataset\/","title":{"rendered":"Building a public COVID-19 dataset of X-ray and CT scans"},"content":{"rendered":"<p><iframe loading=\"lazy\" title=\"COVID-19 image data collection\" width=\"500\" height=\"375\" src=\"https:\/\/www.youtube.com\/embed\/ineWmqfelEQ?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen><\/iframe><\/p>\n<p><span style=\"font-weight: 400;\">In the context of a COVID-19 pandemic, is it crucial to streamline diagnosis. Last year, our team developed Chester, an artificially intelligent (AI) chest X-ray radiology assistant tool that can recognize features such as consolidation, opacity, and edema [<\/span><a href=\"https:\/\/arxiv.org\/abs\/1901.11210\"><span style=\"font-weight: 400;\">Cohen, 2019<\/span><\/a><span style=\"font-weight: 400;\">].<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We now wish to build a public database of pneumonia cases with chest X-ray or CT images, specifically COVID-19 cases as well as MERS, SARS, and ARDS. Data will be collected from public sources as well as through agreements with hospitals and physicians with the consent of their patients.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Our team believes that this database can dramatically improve identification of\u00a0 COVID-19. <\/span><span style=\"font-weight: 400;\">Notably, this would provide essential data to train and test our system. Using the images to develop deep learning based models that can identify COVID-19 characteristic pneumonia, we could ultimately offer a free prototype tool on Chester\u2019s existing platform that could be used by physicians worldwide.<\/span><\/p>\n<h1>Objectives:\u00a0<\/h1>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Build a public open dataset of chest X-ray and CT images of patients which are suspected positive for COVID-19 or other viral and bacterial pneumonias.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Develop methods to make supervised COVID-19 prognostic predictions from chest X-rays and CT scans.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Deploying a prototype of this system using the Chester platform.<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Conduct continuous retrospective and prospective clinical validation of the AI platform using lab validated COVID-19 cases.<\/span><\/li>\n<\/ul>\n<p>The tasks are as follows using chest X-ray or CT (preference for X-ray) as input to predict these tasks:<\/p>\n<ul>\n<li>Healthy vs Pneumonia (prototype already implemented\u00a0<a href=\"https:\/\/mlmed.org\/tools\/xray\/\" rel=\"nofollow\">Chester<\/a> with ~74% AUC, validation study <a href=\"https:\/\/arxiv.org\/abs\/2002.02497\">here<\/a>)<\/li>\n<li><del>Bacterial vs Viral vs COVID-19 Pneumonia<\/del> (not relevant to do anymore)<\/li>\n<li>Survival\/severity of patient<\/li>\n<\/ul>\n<p>An extended writeup is <a href=\"https:\/\/docs.google.com\/document\/d\/1lDx1i2HWjoCHJDuY-v5YPIYuYhYuIIBXryFmhMBK6Wc\/edit\">here<\/a><\/p>\n<p>The dataset website is here: <a href=\"https:\/\/github.com\/ieee8023\/covid-chestxray-dataset\">https:\/\/github.com\/ieee8023\/covid-chestxray-dataset<\/a><\/p>\n<h1>Publications:<\/h1>\n<ul>\n<li>Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., &#038; Ghassemi, M. (2020). COVID-19 Image Data Collection: Prospective Predictions Are the Future. Journal of Machine Learning for Biomedical Imaging (MELBA). https:\/\/github.com\/ieee8023\/covid-chestxray-dataset<\/li>\n<li>Cohen, J. P., Dao, L., Morrison, P., Roth, K., Bengio, Y., Shen, B., Abbasi, A., Hoshmand-Kochi, M., Ghassemi, M., Li, H., &#038; Duong, T. Q. (2020). Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning. Cureus Medical Journal. https:\/\/doi.org\/10.7759\/cureus.9448<\/li>\n<li>Cohen, J. P., Morrison, P., &#038; Dao, L. (2020). COVID-19 Image Data Collection. Https:\/\/Github.Com\/Ieee8023\/Covid-Chestxray-Dataset. https:\/\/arxiv.org\/abs\/2003.11597<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<div class=\"mh-excerpt\"><p>In the context of a COVID-19 pandemic, is it crucial to streamline diagnosis. Last year, our team developed Chester, an artificially intelligent (AI) chest X-ray <a class=\"mh-excerpt-more\" href=\"https:\/\/josephpcohen.com\/w\/public-covid19-dataset\/\" title=\"Building a public COVID-19 dataset of X-ray and CT scans\">[&#8230;]<\/a><\/p>\n<\/div>","protected":false},"author":1,"featured_media":1452,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[16,18],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Building a public COVID-19 dataset of X-ray and CT scans - Joseph Paul Cohen PhD<\/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:\/\/josephpcohen.com\/w\/public-covid19-dataset\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Building a public COVID-19 dataset of X-ray and CT scans - Joseph Paul Cohen PhD\" \/>\n<meta property=\"og:description\" content=\"In the context of a COVID-19 pandemic, is it crucial to streamline diagnosis. 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