{"id":2858,"date":"2024-12-10T12:05:03","date_gmt":"2024-12-10T11:05:03","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=2858"},"modified":"2025-10-29T14:17:01","modified_gmt":"2025-10-29T13:17:01","slug":"digital-pathology-batch-analysis-with-mikaia","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/digital-pathology-batch-analysis-with-mikaia\/","title":{"rendered":"MIKAIA AI Author: Create New AI and Batch-analyze an Entire Dataset"},"content":{"rendered":"\n<p>This video walks you through the entire <strong>Digital Pathology batch analysis<\/strong> workflow from beginning to end:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Load your scans into MIKAIA<sup><sup>\u00ae<\/sup><\/sup>.<\/li>\n\n\n\n<li>Create and train a new AI model with the AI Author.<\/li>\n\n\n\n<li>Analyze your entire dataset with your newly trained AI.<\/li>\n\n\n\n<li>Review the results and exported result files.<\/li>\n<\/ol>\n\n\n\n<p>The AI Author App is used here only as an example. A batch analysis can be conducted just the same with any of the other various apps available in MIKAIA<sup>\u00ae<\/sup>, such as the cell detection apps for IHC, H&amp;E, or immunofluorescence.<\/p>\n\n\n\n<p>To keep it simple, in this tutorial video we show how to train a new AI model for analyzing a set of H&amp;E stained whole-slide-images from The Cancer Genome Atlas (TCGA). We will create here a single-class AI that can recognize connective tissue. It will assign all other tissue that looks too different from the training class into the &#8220;Unsure&#8221; class. Of course, you can train your AI to detect multiple tissue classes.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"1080\" style=\"aspect-ratio: 1920 \/ 1080;\" width=\"1920\" autoplay controls muted src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/AI-Author-batch-analysis-no-sound.mp4\"><\/video><\/figure>\n\n\n\n<p>The AI Author&#8217;s underlying AI technology uses a method called &#8220;Few Shot Learning&#8221;, which means to train an AI to learn something new with only few shots, i.e., few training annotations. The advantage: You do not need to draw hundreds of training annotations. And the training is blazingly fast. Still, to obtain a robust model, it is recommended to train it on multiple slides. Here we show how to do that.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Digital Pathology Batch Analysis<\/h2>\n\n\n\n<p>Once you are happy with the AI model&#8217;s performance, you can then use it to analyze new slides. In the video, we simply select all scans in the dataset and then kick-off the batch analysis by clicking the analyze &#8220;Batch&#8221; button. A job for each slide is created and added to the batch processing queue. You can lean back and let MIKAIA<sup><sup>\u00ae<\/sup><\/sup> do the job. Make sure to enable &#8220;Export results&#8221; and select a target folder. MIKAIA<sup><sup>\u00ae<\/sup><\/sup> will create a set of output files:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>For each slide:\n<ul class=\"wp-block-list\">\n<li>shortcut to original scan<\/li>\n\n\n\n<li>low-resolution (ca 2000x2000px) proxy images with and without burnt-in markup (for quick review)<\/li>\n\n\n\n<li>spreadsheet (*.csv) with results from only that slide<\/li>\n\n\n\n<li>markup file in MIKAIA<sup>\u00ae<\/sup>&#8216;s *.ano file format (can be opened and exported to various other markup formats such as CSV, XML (Leica Aperio format) or GeoJson (loadable by QuPath)<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Once for the entire batch\n<ul class=\"wp-block-list\">\n<li>accumulated spreadsheet (*.csv) with results from all slides. Can be opened in Microsoft Excel, or loaded into R, Python, or any other statistics tools.<\/li>\n\n\n\n<li>config file that contains the used configuration parameters (for documentation and repeatibility)<\/li>\n\n\n\n<li>batch analysis file (in MIKAIA<sup>\u00ae<\/sup>&#8216;s *.micbat format) for later repeating the analysis, if required<\/li>\n\n\n\n<li>in case the AI Author was used: the AI model (in MIKAIA<sup>\u00ae<\/sup>&#8216;s *.ai format).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">AI Author App in detail<\/h2>\n\n\n\n<p>See this app note: <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/mikaia-ai-authoring-app\/\">MIKAIA AI Authoring App<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>MIKAIA\u00ae AI Author used for Digital Pathology batch analysis<\/p>\n","protected":false},"author":4,"featured_media":2855,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,35,28],"tags":[37,7,29,108,111],"coauthors":[57],"class_list":["post-2858","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-pathology","category-life-science","category-mikaia-university","tag-ai","tag-mikaia","tag-mikaia-app-note","tag-video-tutorial","tag-workflow"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Digital Pathology batch analysis with MIKAIA\u00ae AI Author<\/title>\n<meta name=\"description\" content=\"Video tutorial of Digital Pathology batch analysis from start to end: create and train new AI. 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