{"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\/nl\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","title":{"rendered":"Onderzoekers bouwen \"Tyche\" om onzekerheid in medische beeldvorming te omarmen"},"content":{"rendered":"

Medische beeldvorming is een complex vakgebied waar het interpreteren van resultaten een uitdaging kan zijn. <\/strong><\/p>\n

AI-modellen kunnen artsen helpen door beelden te analyseren die kunnen duiden op ziekte-indicerende afwijkingen.<\/p>\n

Er zit echter een addertje onder het gras: deze AI-modellen komen meestal met \u00e9\u00e9n oplossing, terwijl medische beelden in werkelijkheid vaak meerdere interpretaties hebben.<\/p>\n

Als je vijf experts vraagt om een gebied te schetsen dat van belang is, zoals een klein knobbeltje op een longscan, kun je vijf verschillende tekeningen krijgen, omdat ze allemaal hun eigen mening kunnen hebben over waar het knobbeltje bijvoorbeeld begint en eindigt.<\/p>\n

Om dit probleem aan te pakken hebben onderzoekers van MIT, het Broad Institute van MIT Harvard en het Massachusetts General Hospital Tyche gemaakt, een AI-systeem dat de ambigu\u00efteit in medische beeldsegmentatie omarmt.<\/span><\/p>\n

Segmentatie bestaat uit het labelen van specifieke pixels in een medische afbeelding die belangrijke structuren vertegenwoordigen, zoals organen of cellen.\u00a0<\/span><\/p>\n

Marianne Rakic, een MIT-promovenda in computerwetenschappen en hoofdauteur van de <\/span>onderzoek<\/span><\/a>legt uit: \"Opties hebben kan helpen bij het nemen van beslissingen. Zelfs alleen al het zien dat er onzekerheid is in een medisch beeld kan iemands beslissingen be\u00efnvloeden, dus het is belangrijk om rekening te houden met deze onzekerheid.\"<\/span><\/p>\n

Tyche, vernoemd naar de Griekse godin van het toeval, genereert meerdere mogelijke segmentaties voor een enkel medisch beeld om ambigu\u00efteit op te vangen.\u00a0<\/span><\/p>\n

Elke segmentatie benadrukt iets andere regio's, zodat gebruikers de meest geschikte voor hun behoeften kunnen kiezen.\u00a0<\/span><\/p>\n

Rakic vertelt <\/span>MIT Nieuws<\/span><\/a>\"Als je meerdere kandidaten selecteert en ervoor zorgt dat ze van elkaar verschillen, heb je echt een streepje voor.\"<\/span><\/p>\n

Hoe werkt Tyche? Laten we het opsplitsen in vier eenvoudige stappen:<\/span><\/p>\n

    \n
  1. Leren door het voorbeeld te geven<\/b>: Gebruikers geven Tyche een kleine set voorbeeldafbeeldingen, een \"contextset\" genaamd, die de segmentatietaak weergeven die ze willen uitvoeren. Deze voorbeelden kunnen afbeeldingen bevatten die gesegmenteerd zijn door verschillende menselijke experts, waardoor het model de taak en de mogelijke ambigu\u00efteit beter begrijpt.<\/span><\/li>\n
  2. Neuraal netwerk tweaks<\/b>: De onderzoekers pasten een standaard neurale netwerkarchitectuur aan om Tyche in staat te stellen om te gaan met onzekerheid. Ze pasten de lagen van het netwerk aan zodat de potenti\u00eble segmentaties die bij elke stap werden gegenereerd met elkaar en de contextvoorbeelden konden \"communiceren\".<\/span><\/li>\n
  3. Meerdere mogelijkheden<\/b>: Tyche is ontworpen om meerdere voorspellingen uit te voeren op basis van een enkele medische beeldinvoer en de contextset.\u00a0<\/span><\/li>\n
  4. Kwaliteit belonen<\/b>: Het trainingsproces werd aangepast om Tyche te belonen voor het produceren van de best mogelijke voorspelling. Als de gebruiker om vijf voorspellingen vraagt, krijgt hij alle vijf de medische beeldsegmentaties te zien die Tyche heeft geproduceerd, ook al is er misschien \u00e9\u00e9n beter.\u00a0<\/span><\/li>\n<\/ol>\n
    \"AI
    Bovenaan tonen menselijke annotators variaties in het segmenteren van medische beeldoutputs, aangezien er meerdere interpretaties zijn. Traditionele geautomatiseerde technieken (midden) zijn over het algemeen ontworpen voor specifieke taken en genereren een enkele segmentatie per afbeelding. Tyche (onder) daarentegen vangt op vakkundige wijze het scala aan meningsverschillen tussen annotators over verschillende modaliteiten en anatomische structuren, waardoor hertraining of aanpassingen niet nodig zijn. Bron: ArXiv<\/a>.<\/figcaption><\/figure>\n

    Een van de sterkste punten van Tyche is het aanpassingsvermogen. Tyche kan nieuwe segmentatietaken aan zonder opnieuw te moeten worden getraind.\u00a0<\/span><\/p>\n

    Normaal gesproken maken AI-modellen voor medische beeldsegmentatie gebruik van neurale netwerken die uitgebreide training op grote datasets en expertise op het gebied van machinaal leren vereisen.\u00a0<\/span><\/p>\n

    Tyche kan daarentegen \"out of the box\" worden gebruikt voor verschillende taken, van het opsporen van longlaesies op r\u00f6ntgenfoto's tot het identificeren van hersenafwijkingen op MRI's.<\/span><\/p>\n

    Er zijn talloze onderzoeken gedaan naar AI bij medische beeldvorming, waaronder belangrijke doorbraken in <\/span>borstkankerscreening<\/span><\/a> en AI-diagnostiek die <\/span>overeenkomen met<\/span><\/a> of zelfs <\/span>dokters verslaan<\/span><\/a> bij het interpreteren van beelden.\u00a0<\/span><\/p>\n

    Met het oog op de toekomst is het onderzoeksteam van plan om het gebruik van flexibelere contextsets te onderzoeken, mogelijk met inbegrip van tekst of meerdere soorten afbeeldingen.\u00a0<\/span><\/p>\n

    Ze willen ook manieren ontwikkelen om Tyche's slechtste voorspellingen te verbeteren en het systeem in staat te stellen de beste segmentatiekandidaten aan te bevelen.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Medische beeldvorming is een complex vakgebied waar het interpreteren van resultaten een uitdaging kan zijn. AI-modellen kunnen artsen helpen door beelden te analyseren die kunnen duiden op afwijkingen die duiden op ziekte. Er zit echter een addertje onder het gras: deze AI-modellen komen meestal met \u00e9\u00e9n oplossing, terwijl medische beelden in werkelijkheid vaak meerdere interpretaties hebben. Als je vijf experts vraagt om een gebied van belang te schetsen, zoals een klein knobbeltje op een longscan, kun je vijf verschillende tekeningen krijgen, omdat ze allemaal hun eigen mening kunnen hebben over waar het knobbeltje begint en eindigt, bijvoorbeeld. Om dit probleem aan te pakken, hebben onderzoekers van MIT, de<\/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\/nl\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\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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