{"id":85,"date":"2025-04-22T14:15:42","date_gmt":"2025-04-22T14:15:42","guid":{"rendered":"https:\/\/michaelmaynord.net\/?p=85"},"modified":"2025-04-22T14:28:37","modified_gmt":"2025-04-22T14:28:37","slug":"semi-supervised-training-using-cooperative-labeling-of-weakly-annotated-data-for-nodule-detection-in-chest-ct","status":"publish","type":"post","link":"https:\/\/michaelmaynord.net\/index.php\/2025\/04\/22\/semi-supervised-training-using-cooperative-labeling-of-weakly-annotated-data-for-nodule-detection-in-chest-ct\/","title":{"rendered":"Semi-supervised training using cooperative labeling of weakly annotated data for nodule detection in chest CT"},"content":{"rendered":"\n<p>Machine learning algorithms are best trained with large quantities of accurately annotated samples. While natural scene images can often be labeled relatively cheaply and at large scale, obtaining accurate annotations for medical images is both time consuming and expensive. In this study, we propose a cooperative labeling method that allows us to make use of weakly annotated medical imaging data for the training of a machine learning algorithm. As most clinically produced data are weakly-annotated \u2013 produced for use by humans rather than machines and lacking information machine learning depends upon \u2013 this approach allows us to incorporate a wider range of clinical data and thereby increase the training set size.<\/p>\n\n\n\n<p>Read more here: <a href=\"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/abs\/10.1002\/mp.16219\">https:\/\/aapm.onlinelibrary.wiley.com\/doi\/abs\/10.1002\/mp.16219<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning algorithms are best trained with large quantities of accurately annotated samples. While natural scene images can often be labeled relatively cheaply and at large scale, obtaining accurate annotations&#8230; <\/p>\n","protected":false},"author":1,"featured_media":86,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-85","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-currentpublications"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Semi-supervised training using cooperative labeling of weakly annotated data for nodule detection in chest CT - Michael Maynord<\/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:\/\/michaelmaynord.net\/index.php\/2025\/04\/22\/semi-supervised-training-using-cooperative-labeling-of-weakly-annotated-data-for-nodule-detection-in-chest-ct\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Semi-supervised training using cooperative labeling of weakly annotated data for nodule detection in chest CT - Michael Maynord\" \/>\n<meta property=\"og:description\" content=\"Machine learning algorithms are best trained with large quantities of accurately annotated samples. While natural scene images can often be labeled relatively cheaply and at large scale, obtaining accurate annotations...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/michaelmaynord.net\/index.php\/2025\/04\/22\/semi-supervised-training-using-cooperative-labeling-of-weakly-annotated-data-for-nodule-detection-in-chest-ct\/\" \/>\n<meta property=\"og:site_name\" content=\"Michael Maynord\" \/>\n<meta property=\"article:published_time\" content=\"2025-04-22T14:15:42+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-22T14:28:37+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/michaelmaynord.net\/wp-content\/uploads\/2025\/04\/Semi-Supervised.png\" \/>\n\t<meta property=\"og:image:width\" content=\"218\" \/>\n\t<meta property=\"og:image:height\" content=\"283\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/michaelmaynord.net\/index.php\/2025\/04\/22\/semi-supervised-training-using-cooperative-labeling-of-weakly-annotated-data-for-nodule-detection-in-chest-ct\/\",\"url\":\"https:\/\/michaelmaynord.net\/index.php\/2025\/04\/22\/semi-supervised-training-using-cooperative-labeling-of-weakly-annotated-data-for-nodule-detection-in-chest-ct\/\",\"name\":\"Semi-supervised training using cooperative labeling of weakly annotated data for nodule detection in chest CT - 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