{"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\/sv\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","title":{"rendered":"Forskare bygger \"Tyche\" f\u00f6r att hantera os\u00e4kerhet inom medicinsk bildbehandling"},"content":{"rendered":"

Medicinsk bildbehandling \u00e4r ett komplext omr\u00e5de d\u00e4r det kan vara sv\u00e5rt att tolka resultaten. <\/strong><\/p>\n

AI-modeller kan hj\u00e4lpa l\u00e4kare genom att analysera bilder som kan tyda p\u00e5 sjukdomsindikerande avvikelser.<\/p>\n

Det finns dock en hake: dessa AI-modeller kommer vanligtvis fram till en enda l\u00f6sning n\u00e4r medicinska bilder i verkligheten ofta har flera olika tolkningar.<\/p>\n

Om du ber fem experter att skissera ett intresseomr\u00e5de, till exempel en liten kn\u00f6l p\u00e5 en lungr\u00f6ntgen, kan du f\u00e5 fem olika ritningar, eftersom de alla kan ha sina egna \u00e5sikter om var kn\u00f6len b\u00f6rjar och slutar, till exempel.<\/p>\n

F\u00f6r att tackla detta problem har forskare fr\u00e5n MIT, Broad Institute of MIT Harvard och Massachusetts General Hospital skapat Tyche, ett AI-system som tar h\u00e4nsyn till tvetydigheten i medicinsk bildsegmentering.<\/span><\/p>\n

Segmentering inneb\u00e4r att man m\u00e4rker ut specifika pixlar i en medicinsk bild som representerar viktiga strukturer, t.ex. organ eller celler.\u00a0<\/span><\/p>\n

Marianne Rakic, doktorand i datavetenskap vid MIT och huvudf\u00f6rfattare till <\/span>studie<\/span><\/a>\"Att ha alternativ kan underl\u00e4tta beslutsfattandet. Bara att se att det finns en os\u00e4kerhet i en medicinsk bild kan p\u00e5verka n\u00e5gons beslut, s\u00e5 det \u00e4r viktigt att ta h\u00e4nsyn till denna os\u00e4kerhet.\"<\/span><\/p>\n

Tyche, som har f\u00e5tt sitt namn efter den grekiska slumpens gudinna, genererar flera m\u00f6jliga segmenteringar f\u00f6r en enda medicinsk bild f\u00f6r att f\u00e5nga upp tvetydigheter.\u00a0<\/span><\/p>\n

Varje segmentering belyser lite olika regioner, vilket g\u00f6r att anv\u00e4ndarna kan v\u00e4lja den som passar b\u00e4st f\u00f6r deras behov.\u00a0<\/span><\/p>\n

Rakic ber\u00e4ttar <\/span>MIT Nyheter<\/span><\/a>\"Att ta fram flera kandidater och se till att de skiljer sig fr\u00e5n varandra ger dig verkligen en f\u00f6rdel.\"<\/span><\/p>\n

S\u00e5, hur fungerar Tyche? L\u00e5t oss dela upp det i fyra enkla steg:<\/span><\/p>\n

    \n
  1. L\u00e4rande genom exempel<\/b>: Anv\u00e4ndarna ger Tyche en liten upps\u00e4ttning exempelbilder, en s\u00e5 kallad \"context set\", som visar den segmenteringsuppgift de vill utf\u00f6ra. Dessa exempel kan inneh\u00e5lla bilder som segmenterats av olika m\u00e4nskliga experter, vilket hj\u00e4lper modellen att f\u00f6rst\u00e5 uppgiften och potentialen f\u00f6r tvetydighet.<\/span><\/li>\n
  2. Justeringar av neurala n\u00e4tverk<\/b>: Forskarna modifierade en standardarkitektur f\u00f6r neurala n\u00e4tverk f\u00f6r att Tyche skulle kunna hantera os\u00e4kerhet. De justerade n\u00e4tverkets lager s\u00e5 att de potentiella segmenteringar som genererades i varje steg kunde \"kommunicera\" med varandra och med de exempel som st\u00e4llts in i sammanhanget.<\/span><\/li>\n
  3. Flera m\u00f6jligheter<\/b>: Tyche \u00e4r utformad f\u00f6r att ge ut flera f\u00f6ruts\u00e4gelser baserat p\u00e5 en enda medicinsk bild och kontexten.\u00a0<\/span><\/li>\n
  4. Bel\u00f6ning f\u00f6r kvalitet<\/b>: Utbildningsprocessen justerades f\u00f6r att bel\u00f6na Tyche f\u00f6r att producera b\u00e4sta m\u00f6jliga f\u00f6ruts\u00e4gelse. Om anv\u00e4ndaren ber om fem f\u00f6ruts\u00e4gelser kan de se alla fem segmenteringar av medicinska bilder som Tyche har producerat, \u00e4ven om en kanske \u00e4r b\u00e4ttre.\u00a0<\/span><\/li>\n<\/ol>\n
    \"Medicinsk
    L\u00e4ngst upp visar m\u00e4nskliga kommentatorer variationer i segmenteringen av medicinska bilder, eftersom det finns flera olika tolkningar. Traditionella automatiserade tekniker (mitten) \u00e4r i allm\u00e4nhet utformade f\u00f6r specifika uppgifter och genererar en enda segmentering per bild. Tyche (l\u00e4ngst ned) f\u00e5ngar d\u00e4remot p\u00e5 ett skickligt s\u00e4tt upp olika oenigheter mellan kommentatorer inom olika modaliteter och anatomiska strukturer, vilket eliminerar behovet av omskolning eller justeringar. K\u00e4lla: Tyche: ArXiv<\/a>.<\/figcaption><\/figure>\n

    En av Tyches st\u00f6rsta styrkor \u00e4r dess anpassningsf\u00f6rm\u00e5ga. Den kan ta sig an nya segmenteringsuppgifter utan att beh\u00f6va omskolas fr\u00e5n grunden.\u00a0<\/span><\/p>\n

    Normalt anv\u00e4nder AI-modeller f\u00f6r medicinsk bildsegmentering neurala n\u00e4tverk som kr\u00e4ver omfattande tr\u00e4ning p\u00e5 stora datam\u00e4ngder och expertis inom maskininl\u00e4rning.\u00a0<\/span><\/p>\n

    Tyche kan d\u00e4remot anv\u00e4ndas \"direkt\" f\u00f6r olika uppgifter, fr\u00e5n att uppt\u00e4cka lunglesioner p\u00e5 r\u00f6ntgenbilder till att identifiera avvikelser i hj\u00e4rnan p\u00e5 magnetr\u00f6ntgenbilder.<\/span><\/p>\n

    M\u00e5nga studier har genomf\u00f6rts inom medicinsk bildbehandling med AI, inklusive stora genombrott inom <\/span>screening f\u00f6r br\u00f6stcancer<\/span><\/a> och AI-diagnostik som <\/span>match<\/span><\/a> eller till och med <\/span>sl\u00e5 l\u00e4kare<\/span><\/a> vid tolkning av bilder.\u00a0<\/span><\/p>\n

    I framtiden planerar forskargruppen att utforska m\u00f6jligheten att anv\u00e4nda mer flexibla kontextupps\u00e4ttningar, eventuellt med text eller flera olika typer av bilder.\u00a0<\/span><\/p>\n

    De vill ocks\u00e5 utveckla s\u00e4tt att f\u00f6rb\u00e4ttra Tyches s\u00e4msta f\u00f6ruts\u00e4gelser och g\u00f6ra det m\u00f6jligt f\u00f6r systemet att rekommendera de b\u00e4sta segmenteringskandidaterna.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Medicinsk bildbehandling \u00e4r ett komplext omr\u00e5de d\u00e4r det kan vara sv\u00e5rt att tolka resultaten. AI-modeller kan hj\u00e4lpa l\u00e4kare genom att analysera bilder som kan tyda p\u00e5 sjukdomsindikerande avvikelser. Det finns dock en hake: dessa AI-modeller kommer vanligtvis fram till en enda l\u00f6sning n\u00e4r medicinska bilder i sj\u00e4lva verket ofta har flera tolkningar. Om du ber fem experter att beskriva ett intressant omr\u00e5de, t.ex. en liten kn\u00f6l p\u00e5 en lungr\u00f6ntgen, kan du f\u00e5 fem olika ritningar, eftersom de alla kan ha sina egna \u00e5sikter om var kn\u00f6len b\u00f6rjar och slutar, till exempel. F\u00f6r att ta itu med detta problem har forskare fr\u00e5n MIT<\/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\/sv\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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. 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To tackle this problem, researchers from MIT, the\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/sv\/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\" 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