{"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\/nb\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","title":{"rendered":"Forskere bygger \"Tyche\" for \u00e5 h\u00e5ndtere usikkerhet i medisinsk bildebehandling"},"content":{"rendered":"

Medisinsk avbildning er et komplekst felt der det kan v\u00e6re utfordrende \u00e5 tolke resultatene. <\/strong><\/p>\n

AI-modeller kan hjelpe leger ved \u00e5 analysere bilder som kan indikere sykdomsindikerende avvik.<\/p>\n

Det er imidlertid en hake: Disse AI-modellene kommer vanligvis frem til \u00e9n enkelt l\u00f8sning, mens medisinske bilder i virkeligheten ofte har flere tolkninger.<\/p>\n

Hvis du ber fem eksperter om \u00e5 skissere et omr\u00e5de av interesse, for eksempel en liten kul i en lungeskanning, kan du ende opp med fem forskjellige tegninger, ettersom de alle kan ha sine egne meninger om hvor kulen begynner og slutter, for eksempel.<\/p>\n

For \u00e5 l\u00f8se dette problemet har forskere fra MIT, Broad Institute of MIT Harvard og Massachusetts General Hospital utviklet Tyche, et AI-system som tar hensyn til tvetydigheten i medisinsk bildesegmentering.<\/span><\/p>\n

Segmentering inneb\u00e6rer \u00e5 merke bestemte piksler i et medisinsk bilde som representerer viktige strukturer, for eksempel organer eller celler.\u00a0<\/span><\/p>\n

Marianne Rakic, doktorgradskandidat i informatikk ved MIT og hovedforfatter av <\/span>studie<\/span><\/a>\"Det \u00e5 ha valgmuligheter kan v\u00e6re til hjelp i beslutningsprosessen. Bare det \u00e5 se at det er usikkerhet i et medisinsk bilde kan p\u00e5virke beslutningene, s\u00e5 det er viktig \u00e5 ta hensyn til denne usikkerheten.\"<\/span><\/p>\n

Tyche, som er oppkalt etter den greske tilfeldighetsgudinnen, genererer flere mulige segmenteringer for et enkelt medisinsk bilde for \u00e5 fange opp tvetydighet.\u00a0<\/span><\/p>\n

Hver segmentering fremhever litt forskjellige regioner, slik at brukerne kan velge den som passer best for deres behov.\u00a0<\/span><\/p>\n

Rakic forteller <\/span>MIT Nyheter<\/span><\/a>\"Det \u00e5 f\u00e5 ut flere kandidater og s\u00f8rge for at de er forskjellige fra hverandre, gir deg virkelig et fortrinn.\"<\/span><\/p>\n

S\u00e5 hvordan fungerer Tyche? La oss dele det opp i fire enkle trinn:<\/span><\/p>\n

    \n
  1. L\u00e6ring gjennom eksempel<\/b>: Brukerne gir Tyche et lite sett med eksempelbilder, kalt et \"kontekstsett\", som viser segmenteringsoppgaven de \u00f8nsker \u00e5 utf\u00f8re. Disse eksemplene kan inkludere bilder som er segmentert av ulike menneskelige eksperter, noe som hjelper modellen med \u00e5 forst\u00e5 oppgaven og potensialet for tvetydighet.<\/span><\/li>\n
  2. Justeringer av nevrale nettverk<\/b>: Forskerne modifiserte en standard arkitektur for nevrale nettverk slik at Tyche kunne h\u00e5ndtere usikkerhet. De justerte lagene i nettverket slik at de potensielle segmenteringene som ble generert i hvert trinn, kunne \"kommunisere\" med hverandre og med eksemplene i kontekstsettet.<\/span><\/li>\n
  3. Flere muligheter<\/b>: Tyche er utviklet for \u00e5 levere flere prediksjoner basert p\u00e5 ett enkelt medisinsk bilde og kontekstsettet.\u00a0<\/span><\/li>\n
  4. Bel\u00f8nning av kvalitet<\/b>: Oppl\u00e6ringsprosessen ble justert for \u00e5 bel\u00f8nne Tyche for \u00e5 produsere best mulig prediksjon. Hvis brukeren ber om fem prediksjoner, f\u00e5r han eller hun se alle de fem medisinske bildesegmenteringene som Tyche har produsert, selv om \u00e9n kanskje er bedre.\u00a0<\/span><\/li>\n<\/ol>\n
    \"Medisinsk
    \u00d8verst viser menneskelige kommentatorer variasjoner i segmenteringen av medisinske bilder, ettersom det finnes flere tolkninger. Tradisjonelle automatiserte teknikker (i midten) er vanligvis utviklet for spesifikke oppgaver, og genererer \u00e9n enkelt segmentering per bilde. Tyche (nederst) fanger derimot opp alle typer uenigheter blant kommentatorene p\u00e5 tvers av ulike modaliteter og anatomiske strukturer, og eliminerer behovet for omskolering eller justeringer. Kilde: ArXiv<\/a>.<\/figcaption><\/figure>\n

    En av Tyches st\u00f8rste styrker er tilpasningsdyktigheten. Den kan ta p\u00e5 seg nye segmenteringsoppgaver uten \u00e5 m\u00e5tte l\u00e6res opp p\u00e5 nytt fra bunnen av.\u00a0<\/span><\/p>\n

    Normalt bruker AI-modeller for medisinsk bildesegmentering nevrale nettverk som krever omfattende oppl\u00e6ring p\u00e5 store datasett og maskinl\u00e6ringsekspertise.\u00a0<\/span><\/p>\n

    Tyche kan derimot brukes \"out of the box\" til en rekke ulike oppgaver, fra \u00e5 oppdage lungeskader p\u00e5 r\u00f8ntgenbilder til \u00e5 identifisere hjerneabnormaliteter p\u00e5 MR-bilder.<\/span><\/p>\n

    Det er gjennomf\u00f8rt en rekke studier innen medisinsk avbildning med kunstig intelligens, inkludert store gjennombrudd innen <\/span>brystkreftscreening<\/span><\/a> og AI-diagnostikk som <\/span>kamp<\/span><\/a> eller til og med <\/span>sl\u00e5 leger<\/span><\/a> i tolkningen av bilder.\u00a0<\/span><\/p>\n

    I fremtiden planlegger forskerteamet \u00e5 utforske mer fleksible kontekstsett, som muligens kan inkludere tekst eller flere typer bilder.\u00a0<\/span><\/p>\n

    De \u00f8nsker ogs\u00e5 \u00e5 utvikle metoder for \u00e5 forbedre Tyches d\u00e5rligste prediksjoner og gj\u00f8re systemet i stand til \u00e5 anbefale de beste segmenteringskandidatene.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Medisinsk bildebehandling er et komplekst felt der det kan v\u00e6re utfordrende \u00e5 tolke resultatene. AI-modeller kan hjelpe leger ved \u00e5 analysere bilder som kan indikere sykdomsindikerende avvik. Det er imidlertid en hake: Disse AI-modellene kommer vanligvis med \u00e9n enkelt l\u00f8sning, mens medisinske bilder i virkeligheten ofte kan tolkes p\u00e5 flere m\u00e5ter. Hvis du ber fem eksperter om \u00e5 skissere et omr\u00e5de av interesse, for eksempel en liten kul i en lungeskanning, kan du ende opp med fem forskjellige tegninger, ettersom de alle kan ha sine egne meninger om hvor kulen starter og slutter, for eksempel. For \u00e5 l\u00f8se dette problemet har forskere fra MIT, 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\/nb\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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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