{"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\/it\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","title":{"rendered":"I ricercatori costruiscono \"Tyche\" per abbracciare l'incertezza nell'imaging medico"},"content":{"rendered":"

La diagnostica per immagini \u00e8 un campo complesso in cui l'interpretazione dei risultati pu\u00f2 essere impegnativa. <\/strong><\/p>\n

I modelli di intelligenza artificiale possono assistere i medici analizzando le immagini che potrebbero indicare anomalie che portano alla malattia.<\/p>\n

Tuttavia, c'\u00e8 un problema: questi modelli di IA di solito propongono un'unica soluzione quando, in realt\u00e0, le immagini mediche hanno spesso molteplici interpretazioni.<\/p>\n

Se chiedete a cinque esperti di delineare un'area di interesse, come un piccolo nodulo in una scansione polmonare, potreste ritrovarvi con cinque disegni diversi, in quanto tutti potrebbero avere una propria opinione su dove inizia e finisce il nodulo, ad esempio.<\/p>\n

Per affrontare questo problema, i ricercatori del MIT, del Broad Institute del MIT di Harvard e del Massachusetts General Hospital hanno creato Tyche, un sistema di intelligenza artificiale che abbraccia l'ambiguit\u00e0 della segmentazione delle immagini mediche.<\/span><\/p>\n

La segmentazione comporta l'etichettatura di pixel specifici in un'immagine medica che rappresentano strutture importanti, come organi o cellule.\u00a0<\/span><\/p>\n

Marianne Rakic, dottoranda in informatica del MIT e autrice principale della ricerca. <\/span>studio<\/span><\/a>Il medico spiega: \"Avere delle opzioni pu\u00f2 aiutare nel processo decisionale. Anche solo vedere che c'\u00e8 incertezza in un'immagine medica pu\u00f2 influenzare le decisioni di qualcuno, quindi \u00e8 importante tenerne conto\".<\/span><\/p>\n

Chiamata cos\u00ec in onore della dea greca del caso, Tyche genera pi\u00f9 segmentazioni possibili per una singola immagine medica per catturare l'ambiguit\u00e0.\u00a0<\/span><\/p>\n

Ogni segmentazione evidenzia regioni leggermente diverse, consentendo agli utenti di scegliere quella pi\u00f9 adatta alle proprie esigenze.\u00a0<\/span><\/p>\n

Rakic racconta <\/span>Notizie del MIT<\/span><\/a>\"La produzione di pi\u00f9 candidati e la garanzia che siano diversi l'uno dall'altro vi d\u00e0 davvero un vantaggio\".<\/span><\/p>\n

Come funziona Tyche? Vediamo di suddividerlo in quattro semplici passaggi:<\/span><\/p>\n

    \n
  1. Imparare con l'esempio<\/b>: Gli utenti forniscono a Tyche una piccola serie di immagini di esempio, chiamate \"set di contesto\", che mostrano il compito di segmentazione che vogliono eseguire. Questi esempi possono includere immagini segmentate da diversi esperti umani, aiutando il modello a comprendere il compito e le potenziali ambiguit\u00e0.<\/span><\/li>\n
  2. Modifiche alla rete neurale<\/b>: I ricercatori hanno modificato l'architettura di una rete neurale standard per consentire a Tyche di gestire l'incertezza. Hanno regolato gli strati della rete in modo che le segmentazioni potenziali generate in ogni fase potessero \"comunicare\" tra loro e con gli esempi del contesto.<\/span><\/li>\n
  3. Molteplici possibilit\u00e0<\/b>: Tyche \u00e8 progettato per produrre previsioni multiple basate su una singola immagine medica in ingresso e sul set di contesto.\u00a0<\/span><\/li>\n
  4. Premiare la qualit\u00e0<\/b>: Il processo di addestramento \u00e8 stato modificato in modo da premiare Tyche per la produzione della migliore previsione possibile. Se l'utente chiede cinque previsioni, pu\u00f2 vedere tutte e cinque le segmentazioni di immagini mediche prodotte da Tyche, anche se una potrebbe essere migliore.\u00a0<\/span><\/li>\n<\/ol>\n
    \"IA
    In alto, gli annotatori umani mostrano variazioni nella segmentazione dei risultati delle immagini mediche, in quanto esistono molteplici interpretazioni. Le tecniche automatizzate tradizionali (al centro) sono generalmente progettate per compiti specifici e generano una singola segmentazione per immagine. Al contrario, Tyche (in basso) cattura abilmente la gamma di disaccordi degli annotatori tra le varie modalit\u00e0 e strutture anatomiche, eliminando la necessit\u00e0 di riqualificazione o di aggiustamenti. Fonte: ArXiv<\/a>.<\/figcaption><\/figure>\n

    Uno dei maggiori punti di forza di Tyche \u00e8 la sua adattabilit\u00e0. Pu\u00f2 affrontare nuovi compiti di segmentazione senza dover essere riqualificato da zero.\u00a0<\/span><\/p>\n

    Normalmente, i modelli di intelligenza artificiale per la segmentazione delle immagini mediche utilizzano reti neurali che richiedono un addestramento estensivo su grandi serie di dati e competenze di apprendimento automatico.\u00a0<\/span><\/p>\n

    Al contrario, Tyche pu\u00f2 essere utilizzato \"fuori dagli schemi\" per vari compiti, dall'individuazione di lesioni polmonari nelle radiografie all'identificazione di anomalie cerebrali nelle risonanze magnetiche.<\/span><\/p>\n

    Numerosi studi sono stati condotti nel campo dell'imaging medico dell'IA, tra cui importanti scoperte nel campo dell'intelligenza artificiale. <\/span>screening del cancro al seno<\/span><\/a> e diagnostica AI che <\/span>partita<\/span><\/a> o anche <\/span>battere i medici<\/span><\/a> nell'interpretazione delle immagini.\u00a0<\/span><\/p>\n

    Guardando al futuro, il team di ricerca intende esplorare l'utilizzo di set di contesti pi\u00f9 flessibili, possibilmente includendo testo o pi\u00f9 tipi di immagini.\u00a0<\/span><\/p>\n

    Vogliono anche sviluppare modi per migliorare le peggiori previsioni di Tyche e consentire al sistema di raccomandare i migliori candidati alla segmentazione.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    L'imaging medico \u00e8 un campo complesso in cui l'interpretazione dei risultati pu\u00f2 essere impegnativa. I modelli di intelligenza artificiale possono aiutare i medici analizzando le immagini che potrebbero indicare anomalie che portano a malattie. Tuttavia, c'\u00e8 un problema: questi modelli di IA di solito propongono un'unica soluzione quando, in realt\u00e0, le immagini mediche hanno spesso molteplici interpretazioni. Se si chiede a cinque esperti di delineare un'area di interesse, come un piccolo nodulo in una scansione polmonare, si potrebbero ottenere cinque disegni diversi, poich\u00e9 tutti potrebbero avere una propria opinione su dove inizia e finisce il nodulo, ad esempio. Per risolvere questo problema, i ricercatori del MIT, del<\/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\/it\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\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\/it\/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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