{"id":11524,"date":"2024-04-12T18:09:26","date_gmt":"2024-04-12T18:09:26","guid":{"rendered":"https:\/\/dailyai.com\/?p=11524"},"modified":"2024-04-12T18:12:33","modified_gmt":"2024-04-12T18:12:33","slug":"researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging","status":"publish","type":"post","link":"https:\/\/dailyai.com\/fr\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","title":{"rendered":"Des chercheurs construisent \"Tyche\" pour int\u00e9grer l'incertitude dans l'imagerie m\u00e9dicale"},"content":{"rendered":"

L'imagerie m\u00e9dicale est un domaine complexe o\u00f9 l'interpr\u00e9tation des r\u00e9sultats peut s'av\u00e9rer difficile. <\/strong><\/p>\n

Les mod\u00e8les d'IA peuvent aider les m\u00e9decins en analysant les images susceptibles d'indiquer des anomalies pathologiques.<\/p>\n

Mais il y a un hic : ces mod\u00e8les d'IA proposent g\u00e9n\u00e9ralement une solution unique alors qu'en r\u00e9alit\u00e9, les images m\u00e9dicales ont souvent de multiples interpr\u00e9tations.<\/p>\n

Si vous demandez \u00e0 cinq experts d'esquisser une zone d'int\u00e9r\u00eat, comme une petite masse dans un scanner pulmonaire, vous risquez d'obtenir cinq dessins diff\u00e9rents, car ils pourraient tous avoir leur propre opinion sur le point de d\u00e9part et d'arriv\u00e9e de la masse, par exemple.<\/p>\n

Pour r\u00e9soudre ce probl\u00e8me, des chercheurs du MIT, du Broad Institute of MIT Harvard et du Massachusetts General Hospital ont cr\u00e9\u00e9 Tyche, un syst\u00e8me d'intelligence artificielle qui tient compte de l'ambigu\u00eft\u00e9 de la segmentation des images m\u00e9dicales.<\/span><\/p>\n

La segmentation consiste \u00e0 \u00e9tiqueter des pixels sp\u00e9cifiques dans une image m\u00e9dicale qui repr\u00e9sentent des structures importantes, comme des organes ou des cellules.\u00a0<\/span><\/p>\n

Marianne Rakic, candidate au doctorat en informatique au MIT et auteur principal de l'\u00e9tude <\/span>\u00e9tude<\/span><\/a>Il explique : \"Le fait de disposer d'options peut aider \u00e0 la prise de d\u00e9cision. Le simple fait de voir qu'il y a une incertitude dans une image m\u00e9dicale peut influencer les d\u00e9cisions d'une personne, il est donc important de prendre en compte cette incertitude.\"<\/span><\/p>\n

Nomm\u00e9 d'apr\u00e8s la d\u00e9esse grecque du hasard, Tyche g\u00e9n\u00e8re plusieurs segmentations possibles pour une seule image m\u00e9dicale afin d'en saisir l'ambigu\u00eft\u00e9.\u00a0<\/span><\/p>\n

Chaque segmentation met en \u00e9vidence des r\u00e9gions l\u00e9g\u00e8rement diff\u00e9rentes, ce qui permet aux utilisateurs de choisir celle qui convient le mieux \u00e0 leurs besoins.\u00a0<\/span><\/p>\n

Rakic raconte <\/span>Nouvelles du MIT<\/span><\/a>Le fait de produire plusieurs candidats et de s'assurer qu'ils sont diff\u00e9rents les uns des autres vous donne une longueur d'avance.<\/span><\/p>\n

Comment fonctionne Tyche ? D\u00e9composons-le en quatre \u00e9tapes simples :<\/span><\/p>\n

    \n
  1. Apprendre par l'exemple<\/b>: Les utilisateurs donnent \u00e0 Tyche un petit ensemble d'images d'exemple, appel\u00e9 \"ensemble de contexte\", qui montre la t\u00e2che de segmentation qu'ils veulent effectuer. Ces exemples peuvent inclure des images segment\u00e9es par diff\u00e9rents experts humains, ce qui aide le mod\u00e8le \u00e0 comprendre la t\u00e2che et le potentiel d'ambigu\u00eft\u00e9.<\/span><\/li>\n
  2. Ajustements du r\u00e9seau neuronal<\/b>: Les chercheurs ont modifi\u00e9 l'architecture d'un r\u00e9seau neuronal standard pour permettre \u00e0 Tyche de g\u00e9rer l'incertitude. Ils ont ajust\u00e9 les couches du r\u00e9seau pour que les segmentations potentielles g\u00e9n\u00e9r\u00e9es \u00e0 chaque \u00e9tape puissent \"communiquer\" entre elles et avec les exemples de l'ensemble contextuel.<\/span><\/li>\n
  3. De multiples possibilit\u00e9s<\/b>: Tyche est con\u00e7u pour produire des pr\u00e9dictions multiples sur la base d'une seule image m\u00e9dicale et d'un ensemble de contextes.\u00a0<\/span><\/li>\n
  4. R\u00e9compenser la qualit\u00e9<\/b>: Le processus de formation a \u00e9t\u00e9 modifi\u00e9 pour r\u00e9compenser Tyche lorsqu'il produit la meilleure pr\u00e9diction possible. Si l'utilisateur demande cinq pr\u00e9dictions, il peut voir les cinq segmentations d'images m\u00e9dicales produites par Tyche, m\u00eame si l'une d'entre elles est meilleure.\u00a0<\/span><\/li>\n<\/ol>\n
    \"Imagerie
    En haut, des annotateurs humains montrent des variations dans la segmentation des images m\u00e9dicales, car il y a de multiples interpr\u00e9tations. Les techniques automatis\u00e9es traditionnelles (au milieu) sont g\u00e9n\u00e9ralement con\u00e7ues pour des t\u00e2ches sp\u00e9cifiques, g\u00e9n\u00e9rant une seule segmentation par image. En revanche, Tyche (en bas) capture habilement l'\u00e9ventail des d\u00e9saccords entre les annotateurs pour diverses modalit\u00e9s et structures anatomiques, \u00e9liminant ainsi la n\u00e9cessit\u00e9 d'une nouvelle formation ou d'ajustements. Source : ArXiv<\/a>.<\/figcaption><\/figure>\n

    L'un des principaux atouts de Tyche est sa capacit\u00e9 d'adaptation. Il peut prendre en charge de nouvelles t\u00e2ches de segmentation sans avoir besoin d'\u00eatre entra\u00een\u00e9 \u00e0 nouveau \u00e0 partir de z\u00e9ro.\u00a0<\/span><\/p>\n

    Normalement, les mod\u00e8les d'IA pour la segmentation des images m\u00e9dicales utilisent des r\u00e9seaux neuronaux qui n\u00e9cessitent un entra\u00eenement intensif sur de grands ensembles de donn\u00e9es et une expertise en mati\u00e8re d'apprentissage automatique.\u00a0<\/span><\/p>\n

    En revanche, Tyche peut \u00eatre utilis\u00e9 \"pr\u00eat \u00e0 l'emploi\" pour diverses t\u00e2ches, depuis le rep\u00e9rage des l\u00e9sions pulmonaires sur les radiographies jusqu'\u00e0 l'identification des anomalies c\u00e9r\u00e9brales sur les IRM.<\/span><\/p>\n

    De nombreuses \u00e9tudes ont \u00e9t\u00e9 men\u00e9es dans le domaine de l'imagerie m\u00e9dicale par IA, notamment des avanc\u00e9es majeures dans les domaines suivants <\/span>d\u00e9pistage du cancer du sein<\/span><\/a> et des diagnostics d'IA qui <\/span>correspondre<\/span><\/a> ou m\u00eame <\/span>battre les m\u00e9decins<\/span><\/a> dans l'interpr\u00e9tation des images.\u00a0<\/span><\/p>\n

    Pour l'avenir, l'\u00e9quipe de recherche envisage d'utiliser des ensembles de contextes plus souples, comprenant \u00e9ventuellement du texte ou plusieurs types d'images.\u00a0<\/span><\/p>\n

    Ils souhaitent \u00e9galement d\u00e9velopper des moyens d'am\u00e9liorer les pires pr\u00e9dictions de Tyche et permettre au syst\u00e8me de recommander les meilleurs candidats \u00e0 la segmentation.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    L'imagerie m\u00e9dicale est un domaine complexe o\u00f9 l'interpr\u00e9tation des r\u00e9sultats peut s'av\u00e9rer difficile. Les mod\u00e8les d'IA peuvent aider les m\u00e9decins en analysant les images susceptibles d'indiquer des anomalies r\u00e9v\u00e9latrices de maladies. Mais il y a un hic : ces mod\u00e8les d'IA proposent g\u00e9n\u00e9ralement une solution unique alors qu'en r\u00e9alit\u00e9, les images m\u00e9dicales ont souvent de multiples interpr\u00e9tations. Si vous demandez \u00e0 cinq experts d'esquisser une zone d'int\u00e9r\u00eat, comme une petite masse sur un scanner pulmonaire, vous risquez d'obtenir cinq dessins diff\u00e9rents, car chacun peut avoir sa propre opinion sur le point de d\u00e9part et d'arriv\u00e9e de la masse, par exemple. Pour r\u00e9soudre ce probl\u00e8me, des chercheurs du MIT, du<\/p>","protected":false},"author":2,"featured_media":11525,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[203,204,178],"class_list":["post-11524","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-biotech","tag-healthcare","tag-medicine"],"yoast_head":"\nResearchers build "Tyche" to embrace uncertainty in medical imaging | DailyAI<\/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:\/\/dailyai.com\/fr\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Researchers build "Tyche" to embrace uncertainty in medical imaging | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Medical imaging is a complex field where interpreting results can be challenging. AI models can assist doctors by analyzing images that might indicate disease-indicating anomalies. However, there’s a catch: these AI models usually come up with a single solution when, in reality, medical images often have multiple interpretations. If you ask five experts to outline an area of interest, like a small lump in a lung scan, you might end up with five different drawings, as they could all have their own opinions on where the lump starts and ends, for example. To tackle this problem, researchers from MIT, the\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/fr\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-04-12T18:09:26+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-04-12T18:12:33+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/04\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1792\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Sam Jeans\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:site\" content=\"@DailyAIOfficial\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Sam Jeans\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/\"},\"author\":{\"name\":\"Sam Jeans\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/#\\\/schema\\\/person\\\/711e81f945549438e8bbc579efdeb3c9\"},\"headline\":\"Researchers build “Tyche” to embrace uncertainty in medical imaging\",\"datePublished\":\"2024-04-12T18:09:26+00:00\",\"dateModified\":\"2024-04-12T18:12:33+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/\"},\"wordCount\":624,\"publisher\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/dailyai.com\\\/wp-content\\\/uploads\\\/2024\\\/04\\\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp\",\"keywords\":[\"Biotech\",\"Healthcare\",\"Medicine\"],\"articleSection\":[\"Ethics & Society\"],\"inLanguage\":\"fr-FR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/\",\"url\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/\",\"name\":\"Researchers build \\\"Tyche\\\" to embrace uncertainty in medical imaging | DailyAI\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/dailyai.com\\\/wp-content\\\/uploads\\\/2024\\\/04\\\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp\",\"datePublished\":\"2024-04-12T18:09:26+00:00\",\"dateModified\":\"2024-04-12T18:12:33+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/#primaryimage\",\"url\":\"https:\\\/\\\/dailyai.com\\\/wp-content\\\/uploads\\\/2024\\\/04\\\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp\",\"contentUrl\":\"https:\\\/\\\/dailyai.com\\\/wp-content\\\/uploads\\\/2024\\\/04\\\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp\",\"width\":1792,\"height\":1024,\"caption\":\"medical imaging\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/2024\\\/04\\\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/dailyai.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Researchers build “Tyche” to embrace uncertainty in medical imaging\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/#website\",\"url\":\"https:\\\/\\\/dailyai.com\\\/\",\"name\":\"DailyAI\",\"description\":\"Your Daily Dose of AI News\",\"publisher\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/dailyai.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/#organization\",\"name\":\"DailyAI\",\"url\":\"https:\\\/\\\/dailyai.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/dailyai.com\\\/wp-content\\\/uploads\\\/2023\\\/06\\\/Daily-Ai_TL_colour.png\",\"contentUrl\":\"https:\\\/\\\/dailyai.com\\\/wp-content\\\/uploads\\\/2023\\\/06\\\/Daily-Ai_TL_colour.png\",\"width\":4501,\"height\":934,\"caption\":\"DailyAI\"},\"image\":{\"@id\":\"https:\\\/\\\/dailyai.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/x.com\\\/DailyAIOfficial\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/dailyaiofficial\\\/\",\"https:\\\/\\\/www.youtube.com\\\/@DailyAIOfficial\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/dailyai.com\\\/#\\\/schema\\\/person\\\/711e81f945549438e8bbc579efdeb3c9\",\"name\":\"Sam Jeans\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/a24a4a8f8e2a1a275b7491dc9c9f032c401eabf23c3206da4628dc84b6dac5c8?s=96&d=robohash&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/a24a4a8f8e2a1a275b7491dc9c9f032c401eabf23c3206da4628dc84b6dac5c8?s=96&d=robohash&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/a24a4a8f8e2a1a275b7491dc9c9f032c401eabf23c3206da4628dc84b6dac5c8?s=96&d=robohash&r=g\",\"caption\":\"Sam Jeans\"},\"description\":\"Sam is a science and technology writer who has worked in various AI startups. When he\u2019s not writing, he can be found reading medical journals or digging through boxes of vinyl records.\",\"sameAs\":[\"https:\\\/\\\/www.linkedin.com\\\/in\\\/sam-jeans-6746b9142\\\/\"],\"url\":\"https:\\\/\\\/dailyai.com\\\/fr\\\/author\\\/samjeans\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Des chercheurs construisent \"Tyche\" pour int\u00e9grer l'incertitude dans l'imagerie m\u00e9dicale | DailyAI","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/dailyai.com\/fr\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","og_locale":"fr_FR","og_type":"article","og_title":"Researchers build \"Tyche\" to embrace uncertainty in medical imaging | DailyAI","og_description":"Medical imaging is a complex field where interpreting results can be challenging. AI models can assist doctors by analyzing images that might indicate disease-indicating anomalies. However, there’s a catch: these AI models usually come up with a single solution when, in reality, medical images often have multiple interpretations. If you ask five experts to outline an area of interest, like a small lump in a lung scan, you might end up with five different drawings, as they could all have their own opinions on where the lump starts and ends, for example. To tackle this problem, researchers from MIT, the","og_url":"https:\/\/dailyai.com\/fr\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","og_site_name":"DailyAI","article_published_time":"2024-04-12T18:09:26+00:00","article_modified_time":"2024-04-12T18:12:33+00:00","og_image":[{"width":1792,"height":1024,"url":"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/04\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp","type":"image\/webp"}],"author":"Sam Jeans","twitter_card":"summary_large_image","twitter_creator":"@DailyAIOfficial","twitter_site":"@DailyAIOfficial","twitter_misc":{"\u00c9crit par":"Sam Jeans","Dur\u00e9e de lecture estim\u00e9e":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"NewsArticle","@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/#article","isPartOf":{"@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/"},"author":{"name":"Sam Jeans","@id":"https:\/\/dailyai.com\/#\/schema\/person\/711e81f945549438e8bbc579efdeb3c9"},"headline":"Researchers build “Tyche” to embrace uncertainty in medical imaging","datePublished":"2024-04-12T18:09:26+00:00","dateModified":"2024-04-12T18:12:33+00:00","mainEntityOfPage":{"@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/"},"wordCount":624,"publisher":{"@id":"https:\/\/dailyai.com\/#organization"},"image":{"@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/#primaryimage"},"thumbnailUrl":"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/04\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp","keywords":["Biotech","Healthcare","Medicine"],"articleSection":["Ethics & Society"],"inLanguage":"fr-FR"},{"@type":"WebPage","@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","url":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","name":"Des chercheurs construisent \"Tyche\" pour int\u00e9grer l'incertitude dans l'imagerie m\u00e9dicale | DailyAI","isPartOf":{"@id":"https:\/\/dailyai.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/#primaryimage"},"image":{"@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/#primaryimage"},"thumbnailUrl":"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/04\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp","datePublished":"2024-04-12T18:09:26+00:00","dateModified":"2024-04-12T18:12:33+00:00","breadcrumb":{"@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/#primaryimage","url":"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/04\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp","contentUrl":"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/04\/DALL\u00b7E-2024-04-12-19.06.14-A-high-quality-landscape-oriented-image-of-an-advanced-medical-imaging-room-showcasing-a-state-of-the-art-MRI-machine-at-the-center.-The-room-is-ill.webp","width":1792,"height":1024,"caption":"medical imaging"},{"@type":"BreadcrumbList","@id":"https:\/\/dailyai.com\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/dailyai.com\/"},{"@type":"ListItem","position":2,"name":"Researchers build “Tyche” to embrace uncertainty in medical imaging"}]},{"@type":"WebSite","@id":"https:\/\/dailyai.com\/#website","url":"https:\/\/dailyai.com\/","name":"DailyAI","description":"Votre dose quotidienne de nouvelles sur l'IA","publisher":{"@id":"https:\/\/dailyai.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/dailyai.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/dailyai.com\/#organization","name":"DailyAI","url":"https:\/\/dailyai.com\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/dailyai.com\/#\/schema\/logo\/image\/","url":"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/Daily-Ai_TL_colour.png","contentUrl":"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/06\/Daily-Ai_TL_colour.png","width":4501,"height":934,"caption":"DailyAI"},"image":{"@id":"https:\/\/dailyai.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/DailyAIOfficial","https:\/\/www.linkedin.com\/company\/dailyaiofficial\/","https:\/\/www.youtube.com\/@DailyAIOfficial"]},{"@type":"Person","@id":"https:\/\/dailyai.com\/#\/schema\/person\/711e81f945549438e8bbc579efdeb3c9","name":"Sam Jeans","image":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/secure.gravatar.com\/avatar\/a24a4a8f8e2a1a275b7491dc9c9f032c401eabf23c3206da4628dc84b6dac5c8?s=96&d=robohash&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/a24a4a8f8e2a1a275b7491dc9c9f032c401eabf23c3206da4628dc84b6dac5c8?s=96&d=robohash&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/a24a4a8f8e2a1a275b7491dc9c9f032c401eabf23c3206da4628dc84b6dac5c8?s=96&d=robohash&r=g","caption":"Sam Jeans"},"description":"Sam est un r\u00e9dacteur scientifique et technologique qui a travaill\u00e9 dans diverses start-ups sp\u00e9cialis\u00e9es dans l'IA. Lorsqu'il n'\u00e9crit pas, on peut le trouver en train de lire des revues m\u00e9dicales ou de fouiller dans des bo\u00eetes de disques vinyles.","sameAs":["https:\/\/www.linkedin.com\/in\/sam-jeans-6746b9142\/"],"url":"https:\/\/dailyai.com\/fr\/author\/samjeans\/"}]}},"_links":{"self":[{"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/posts\/11524","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/comments?post=11524"}],"version-history":[{"count":3,"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/posts\/11524\/revisions"}],"predecessor-version":[{"id":11529,"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/posts\/11524\/revisions\/11529"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/media\/11525"}],"wp:attachment":[{"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/media?parent=11524"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/categories?post=11524"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dailyai.com\/fr\/wp-json\/wp\/v2\/tags?post=11524"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}