{"id":184996,"date":"2010-05-18T00:00:00","date_gmt":"2010-08-05T14:57:37","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/statistical-learning-without-ground-truth-in-computer-vision-and-medicine\/"},"modified":"2016-08-22T11:28:42","modified_gmt":"2016-08-22T18:28:42","slug":"statistical-learning-without-ground-truth-in-computer-vision-and-medicine","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/statistical-learning-without-ground-truth-in-computer-vision-and-medicine\/","title":{"rendered":"Statistical Learning without Ground Truth in Computer Vision and Medicine"},"content":{"rendered":"
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The first part of the talk will present and overview of our machine learning endeavors within the scope of Computational Pathology: (i) generating a gold standard based on clinical labeling experiments, (ii) using off-line and on-line ensemble learning techniques to train object detectors for cell nuclei and (iii) employing Bayesian survival statistics for biomarker detection and diagnosing cancer patients. Based on several interdisciplinary research projects I will demonstrate how insights gained from statistical modeling can be translated to biomedical knowledge and in which ways clinical decision making can benefit from it.<\/p>\n

In the second part of the talk I am going to give an outlook on learning under labeling uncertainty: In a large number of real world application an objective ground truth is not available or too expensive to acquire. In practice, as a last resort, one would ask several domain experts for their opinion about each object in question to generate a gold standard. Depending on the difficulty of the task this often results in ambiguous labeling due to disagreement between experts. The resulting labeling matrix poses a non-trivial challenge for supervised learning. We will investigate under which condition it is possible to learn more about the data generating distribution than just using majority vote. A positive result would have immense influence in domains where specific models can be trained for years by a large number of experts, e.g. medical decision support. I will illustrate the problem with examples from medicine and space exploration, i.e. the classification of cell nuclei and the recognition of volcanoes on Venus.<\/p>\n<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

The first part of the talk will present and overview of our machine learning endeavors within the scope of Computational Pathology: (i) generating a gold standard based on clinical labeling experiments, (ii) using off-line and on-line ensemble learning techniques to train object detectors for cell nuclei and (iii) employing Bayesian survival statistics for biomarker detection […]<\/p>\n","protected":false},"featured_media":281021,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-184996","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/wNpilOFtb_c","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/184996","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/184996\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/281021"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=184996"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=184996"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=184996"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=184996"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=184996"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=184996"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=184996"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=184996"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=184996"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=184996"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}