{"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\/da\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","title":{"rendered":"Forskere bygger \"Tyche\" til at omfavne usikkerhed i medicinsk billeddannelse"},"content":{"rendered":"

Medicinsk billeddannelse er et komplekst omr\u00e5de, hvor det kan v\u00e6re sv\u00e6rt at fortolke resultater. <\/strong><\/p>\n

AI-modeller kan hj\u00e6lpe l\u00e6ger ved at analysere billeder, der kan indikere sygdomsindikerende anomalier.<\/p>\n

Der er dog en hage: Disse AI-modeller kommer normalt med en enkelt l\u00f8sning, n\u00e5r medicinske billeder i virkeligheden ofte har flere fortolkninger.<\/p>\n

Hvis du beder fem eksperter om at skitsere et interesseomr\u00e5de, f.eks. en lille knude i en lungescanning, kan du ende med fem forskellige tegninger, da de alle kan have deres egen mening om, hvor knuden f.eks. starter og slutter.<\/p>\n

For at l\u00f8se dette problem har forskere fra MIT, Broad Institute of MIT Harvard og Massachusetts General Hospital skabt Tyche, et AI-system, der tager h\u00f8jde for tvetydigheden i medicinsk billedsegmentering.<\/span><\/p>\n

Segmentering indeb\u00e6rer m\u00e6rkning af specifikke pixels i et medicinsk billede, der repr\u00e6senterer vigtige strukturer, som f.eks. organer eller celler.\u00a0<\/span><\/p>\n

Marianne Rakic, ph.d.-kandidat i datalogi ved MIT og hovedforfatter til projektet <\/span>unders\u00f8gelse<\/span><\/a>\"At have valgmuligheder kan hj\u00e6lpe med at tr\u00e6ffe beslutninger. Bare det at se, at der er usikkerhed i et medicinsk billede, kan p\u00e5virke nogens beslutninger, s\u00e5 det er vigtigt at tage h\u00f8jde for denne usikkerhed.\"<\/span><\/p>\n

Tyche er opkaldt efter den gr\u00e6ske tilf\u00e6ldighedsgudinde og genererer flere mulige segmenteringer for et enkelt medicinsk billede for at fange tvetydighed.\u00a0<\/span><\/p>\n

Hver segmentering fremh\u00e6ver lidt forskellige regioner, s\u00e5 brugerne kan v\u00e6lge den, der passer bedst til deres behov.\u00a0<\/span><\/p>\n

Rakic fort\u00e6ller <\/span>MIT-nyheder<\/span><\/a>\"At f\u00e5 flere kandidater ud og sikre, at de er forskellige fra hinanden, giver dig virkelig en fordel.\"<\/span><\/p>\n

S\u00e5 hvordan fungerer Tyche? Lad os dele det op i fire enkle trin:<\/span><\/p>\n

    \n
  1. L\u00e6ring ved hj\u00e6lp af eksempler<\/b>: Brugerne giver Tyche et lille s\u00e6t eksempler p\u00e5 billeder, kaldet et \"konteksts\u00e6t\", der viser den segmenteringsopgave, de \u00f8nsker at udf\u00f8re. Disse eksempler kan omfatte billeder, der er segmenteret af forskellige menneskelige eksperter, hvilket hj\u00e6lper modellen med at forst\u00e5 opgaven og potentialet for tvetydighed.<\/span><\/li>\n
  2. Justeringer af neurale netv\u00e6rk<\/b>: Forskerne \u00e6ndrede en standard arkitektur for neurale netv\u00e6rk, s\u00e5 Tyche kunne h\u00e5ndtere usikkerhed. De justerede netv\u00e6rkets lag, s\u00e5 de potentielle segmenteringer, der blev genereret p\u00e5 hvert trin, kunne \"kommunikere\" med hinanden og med eksemplerne i konteksts\u00e6ttet.<\/span><\/li>\n
  3. Flere muligheder<\/b>: Tyche er designet til at udsende flere forudsigelser baseret p\u00e5 et enkelt medicinsk billedinput og konteksts\u00e6ttet.\u00a0<\/span><\/li>\n
  4. Bel\u00f8nning af kvalitet<\/b>: Tr\u00e6ningsprocessen blev justeret for at bel\u00f8nne Tyche for at producere den bedst mulige forudsigelse. Hvis brugeren beder om fem forudsigelser, kan de se alle fem medicinske billedsegmenteringer produceret af Tyche, selv om en m\u00e5ske er bedre.\u00a0<\/span><\/li>\n<\/ol>\n
    \"Medicinsk
    \u00d8verst viser menneskelige kommentatorer variationer i segmenteringen af medicinske billeder, da der er flere fortolkninger. Traditionelle automatiserede teknikker (i midten) er generelt designet til specifikke opgaver og genererer en enkelt segmentering pr. billede. I mods\u00e6tning hertil indfanger Tyche (nederst) dygtigt r\u00e6kkevidden af uenigheder mellem kommentatorer p\u00e5 tv\u00e6rs af forskellige modaliteter og anatomiske strukturer, hvilket eliminerer behovet for omskoling eller justeringer. Kilde: ArXiv<\/a>.<\/figcaption><\/figure>\n

    En af Tyches st\u00f8rste styrker er dens tilpasningsevne. Den kan p\u00e5tage sig nye segmenteringsopgaver uden at skulle genoptr\u00e6nes fra bunden.\u00a0<\/span><\/p>\n

    Normalt bruger AI-modeller til medicinsk billedsegmentering neurale netv\u00e6rk, som kr\u00e6ver omfattende tr\u00e6ning p\u00e5 store datas\u00e6t og ekspertise inden for maskinl\u00e6ring.\u00a0<\/span><\/p>\n

    I mods\u00e6tning hertil kan Tyche bruges \"out of the box\" til forskellige opgaver, lige fra at spotte lungel\u00e6sioner i r\u00f8ntgenbilleder til at identificere hjerneabnormaliteter i MR-scanninger.<\/span><\/p>\n

    Der er gennemf\u00f8rt adskillige unders\u00f8gelser inden for medicinsk billeddannelse med kunstig intelligens, herunder store gennembrud inden for <\/span>screening for brystkr\u00e6ft<\/span><\/a> og AI-diagnostik, der <\/span>match<\/span><\/a> eller endda <\/span>sl\u00e5 l\u00e6ger<\/span><\/a> i fortolkningen af billeder.\u00a0<\/span><\/p>\n

    I fremtiden planl\u00e6gger forskerteamet at udforske brugen af mere fleksible konteksts\u00e6t, muligvis med tekst eller flere typer billeder.\u00a0<\/span><\/p>\n

    De vil ogs\u00e5 udvikle m\u00e5der at forbedre Tyches v\u00e6rste forudsigelser p\u00e5 og g\u00f8re det muligt for systemet at anbefale de bedste segmenteringskandidater.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Medicinsk billeddannelse er et komplekst omr\u00e5de, hvor det kan v\u00e6re sv\u00e6rt at fortolke resultaterne. AI-modeller kan hj\u00e6lpe l\u00e6ger ved at analysere billeder, der kan indikere sygdomsfremkaldende anomalier. Der er dog en hage: Disse AI-modeller kommer normalt med en enkelt l\u00f8sning, mens medicinske billeder i virkeligheden ofte har flere fortolkninger. Hvis du beder fem eksperter om at skitsere et interessant omr\u00e5de, f.eks. en lille knude p\u00e5 en lungescanning, kan du ende med fem forskellige tegninger, da de alle kan have deres egen mening om, hvor knuden f.eks. starter og slutter. For at tackle dette problem har forskere fra MIT, the<\/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\/da\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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\/da\/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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