{"id":3133,"date":"2023-11-02T08:29:41","date_gmt":"2023-11-02T08:29:41","guid":{"rendered":"https:\/\/caimed.de\/?page_id=3133"},"modified":"2023-11-22T12:52:28","modified_gmt":"2023-11-22T12:52:28","slug":"ki-signale","status":"publish","type":"page","link":"https:\/\/caimed.de\/en\/forschung\/ki-signale\/","title":{"rendered":"AI & Signals"},"content":{"rendered":"
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AI & Signals<\/h1>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Image and biosignal data are becoming increasingly important in clinical diagnostics and research. Echocardiography sequences and longitudinal image studies for patient monitoring can be analyzed using new machine learning methods, for example, to extract relevant markers and use them for prediction models in cardiology. Self-supervised learning methods are also used to extract relevant markers from large image and signal data without expensive expert labels, as well as cross-modal learning methods that combine image data with sensor data or textual information (findings). The \"AI and signals\" cluster deals with the application and further development of AI methods for image, video and biosignals and time series. Building on this, the junior research groups Machine Learning for Non-Textual Data, Biomedical Image Recognition and AI in Oncology - Focus on Image Data described below will be established. <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

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\n \n \n AI & Signals<\/span>\n
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Machine Learning<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Biomedical Image Recognition<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Collab. Development & Validation<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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\n \n \n AI & Semantics<\/span>\n
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AI & Causality<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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AI & Bioinformatics<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Multi-Omics Data<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Statistical Evidence<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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\n \n \n AI & Decisions<\/span>\n
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Human-Centered AI<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Clinical Decision Support<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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\n \n \n AI & Active Agents<\/span>\n
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Semantic Models<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Mathematical Models<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Digital Twins<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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\n \n \n AI & Signals<\/span>\n
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Machine Learning<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Biomedical Image Recognition<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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Collab. Development & Validation<\/h6>\n <\/a>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

KI & Signale Bild- und Biosignaldaten werden immer wichtiger in der klinischen Diagnostik und Forschung. Echokardiografie-Sequenzen und longitudinale Bildstudien f\u00fcr das Monitoring von Patienten k\u00f6nnen etwa mit neuen Machine Learning-Verfahren analysiert werden, um relevante Marker zu extrahieren und f\u00fcr Pr\u00e4diktionsmodelle in der Kardiologie zu nutzen. Auch werden selbst\u00fcberwachte Lernverfahren eingesetzt, um aus gro\u00dfen Bild- und Signaldaten […]<\/p>","protected":false},"author":4,"featured_media":0,"parent":2907,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width-page.php","meta":{"footnotes":""},"class_list":["post-3133","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/3133","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/comments?post=3133"}],"version-history":[{"count":5,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/3133\/revisions"}],"predecessor-version":[{"id":5161,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/3133\/revisions\/5161"}],"up":[{"embeddable":true,"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/pages\/2907"}],"wp:attachment":[{"href":"https:\/\/caimed.de\/en\/wp-json\/wp\/v2\/media?parent=3133"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}