{"id":792212,"date":"2021-11-16T08:00:27","date_gmt":"2021-11-16T16:00:27","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=792212"},"modified":"2021-11-17T09:17:59","modified_gmt":"2021-11-17T17:17:59","slug":"research-talk-causality-for-medical-image-analysis","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/research-talk-causality-for-medical-image-analysis\/","title":{"rendered":"Research talk: Causality for medical image analysis"},"content":{"rendered":"<p>Machine learning has huge potential to augment medical image analysis workflows and improve patient care. However, two of its notorious real-world challenges are the difficulty in acquiring sufficient, high-quality annotated data and mismatches between the development dataset and the target environment (across hospitals, for example).<\/p>\n<p>Daniel Coelho de Castro, a researcher in the Health Intelligence Group at Microsoft Research Cambridge, will discuss how causal reasoning can shed new light on these pervasive issues and appropriate mitigation strategies. In particular, a causal perspective enables decisions about data collection, annotation, pre-processing, and learning strategies to be made\u2014and scrutinized\u2014more transparently. He will highlight how understanding and communicating the story behind the data helps improve the reliability of machine learning systems in high-risk healthcare settings. This session will cover a causal categorization of potential biases when developing medical imaging models, a couple of worked clinical examples, and step-by-step recommendations for practitioners.<\/p>\n<p>Learn more about the 2021 Microsoft Research Summit: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/Aka.ms\/researchsummit\">https:\/\/Aka.ms\/researchsummit<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning has huge potential to augment medical image analysis workflows and improve patient care. However, two of its notorious real-world challenges are the difficulty in acquiring sufficient, high-quality annotated data and mismatches between the development dataset and the target environment (across hospitals, for example). Daniel Coelho de Castro, a researcher in the Health Intelligence [&hellip;]<\/p>\n","protected":false},"featured_media":792215,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13553],"msr-video-type":[261317,261263],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-792212","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-medical-health-genomics","msr-video-type-causal-machine-learning","msr-video-type-research-summit-2021","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/l1gRozZZBBI","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/792212"}],"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":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/792212\/revisions"}],"predecessor-version":[{"id":796996,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/792212\/revisions\/796996"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/792215"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=792212"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=792212"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=792212"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=792212"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=792212"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=792212"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=792212"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}