{"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\/de\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","title":{"rendered":"Forscher bauen \"Tyche\", um Unsicherheiten in der medizinischen Bildgebung auszugleichen"},"content":{"rendered":"

Die medizinische Bildgebung ist ein komplexer Bereich, in dem die Interpretation der Ergebnisse schwierig sein kann. <\/strong><\/p>\n

KI-Modelle k\u00f6nnen \u00c4rzte bei der Analyse von Bildern unterst\u00fctzen, die auf krankheitsrelevante Anomalien hinweisen k\u00f6nnten.<\/p>\n

Die Sache hat jedoch einen Haken: Diese KI-Modelle bieten in der Regel nur eine einzige L\u00f6sung an, w\u00e4hrend medizinische Bilder in der Realit\u00e4t oft mehrere Interpretationen zulassen.<\/p>\n

Wenn Sie f\u00fcnf Experten bitten, einen Bereich von Interesse zu skizzieren, z. B. einen kleinen Knoten in einem Lungenscan, erhalten Sie m\u00f6glicherweise f\u00fcnf verschiedene Zeichnungen, da jeder von ihnen seine eigene Meinung dazu hat, wo der Knoten beginnt und wo er endet.<\/p>\n

Um dieses Problem zu l\u00f6sen, haben Forscher des MIT, des Broad Institute of MIT Harvard und des Massachusetts General Hospital Tyche entwickelt, ein KI-System, das die Mehrdeutigkeit bei der Segmentierung medizinischer Bilder ber\u00fccksichtigt.<\/span><\/p>\n

Bei der Segmentierung geht es um die Kennzeichnung bestimmter Pixel in einem medizinischen Bild, die wichtige Strukturen, wie Organe oder Zellen, darstellen.\u00a0<\/span><\/p>\n

Marianne Rakic, Doktorandin der Computerwissenschaften am MIT und Hauptautorin der Studie <\/span>Studie<\/span><\/a>Er erkl\u00e4rt: \"Optionen zu haben, kann bei der Entscheidungsfindung helfen. Schon allein der Umstand, dass ein medizinisches Bild mit Unsicherheiten behaftet ist, kann die Entscheidungen beeinflussen, daher ist es wichtig, diese Unsicherheit zu ber\u00fccksichtigen.<\/span><\/p>\n

Tyche, benannt nach der griechischen G\u00f6ttin des Zufalls, erzeugt mehrere m\u00f6gliche Segmentierungen f\u00fcr ein einzelnes medizinisches Bild, um Mehrdeutigkeiten zu erfassen.\u00a0<\/span><\/p>\n

Jede Segmentierung hebt leicht unterschiedliche Regionen hervor, so dass die Nutzer die f\u00fcr ihre Bed\u00fcrfnisse am besten geeignete ausw\u00e4hlen k\u00f6nnen.\u00a0<\/span><\/p>\n

Rakic erz\u00e4hlt <\/span>MIT-Nachrichten<\/span><\/a>Wenn man mehrere Kandidaten ausgibt und sicherstellt, dass sie sich voneinander unterscheiden, hat man einen echten Vorteil.<\/span><\/p>\n

Wie funktioniert Tyche also? Lassen Sie es uns in vier einfache Schritte unterteilen:<\/span><\/p>\n

    \n
  1. Lernen durch Vorbild<\/b>: Die Benutzer geben Tyche eine kleine Menge von Beispielbildern, die so genannte \"Kontextmenge\", die die gew\u00fcnschte Segmentierungsaufgabe zeigen. Diese Beispiele k\u00f6nnen Bilder enthalten, die von verschiedenen menschlichen Experten segmentiert wurden, um dem Modell zu helfen, die Aufgabe und das Potenzial f\u00fcr Mehrdeutigkeit zu verstehen.<\/span><\/li>\n
  2. Optimierungen des neuronalen Netzes<\/b>: Die Forscher \u00e4nderten eine Standardarchitektur eines neuronalen Netzes, damit Tyche mit Unsicherheiten umgehen kann. Sie passten die Schichten des Netzes so an, dass die in jedem Schritt erzeugten potenziellen Segmentierungen miteinander und mit den Kontextbeispielen \"kommunizieren\" konnten.<\/span><\/li>\n
  3. Mehrere M\u00f6glichkeiten<\/b>: Tyche ist so konzipiert, dass es mehrere Vorhersagen auf der Grundlage eines einzigen medizinischen Bildes und der Kontextmenge ausgibt.\u00a0<\/span><\/li>\n
  4. Qualit\u00e4t wird belohnt<\/b>: Der Trainingsprozess wurde so optimiert, dass Tyche f\u00fcr die bestm\u00f6gliche Vorhersage belohnt wird. Wenn der Nutzer f\u00fcnf Vorhersagen anfordert, kann er alle f\u00fcnf von Tyche erstellten Segmentierungen medizinischer Bilder sehen, auch wenn eine davon besser sein k\u00f6nnte.\u00a0<\/span><\/li>\n<\/ol>\n
    \"Medizinische
    Oben zeigen menschliche Annotatoren Unterschiede bei der Segmentierung medizinischer Bilder, da es mehrere Interpretationen gibt. Herk\u00f6mmliche automatisierte Verfahren (Mitte) sind in der Regel f\u00fcr bestimmte Aufgaben konzipiert und erzeugen eine einzige Segmentierung pro Bild. Im Gegensatz dazu erfasst Tyche (unten) geschickt die Bandbreite der Unstimmigkeiten zwischen den Kommentatoren \u00fcber verschiedene Modalit\u00e4ten und anatomische Strukturen hinweg, so dass ein erneutes Training oder Anpassungen nicht erforderlich sind. Quelle: ArXiv<\/a>.<\/figcaption><\/figure>\n

    Eine der gr\u00f6\u00dften St\u00e4rken von Tyche ist seine Anpassungsf\u00e4higkeit. Es kann neue Segmentierungsaufgaben \u00fcbernehmen, ohne dass es von Grund auf neu geschult werden muss.\u00a0<\/span><\/p>\n

    Normalerweise verwenden KI-Modelle f\u00fcr die Segmentierung medizinischer Bilder neuronale Netze, die umfangreiches Training auf gro\u00dfen Datens\u00e4tzen und Fachwissen \u00fcber maschinelles Lernen erfordern.\u00a0<\/span><\/p>\n

    Im Gegensatz dazu kann Tyche \"out of the box\" f\u00fcr verschiedene Aufgaben eingesetzt werden, von der Erkennung von Lungenl\u00e4sionen in R\u00f6ntgenbildern bis zur Identifizierung von Hirnanomalien in MRTs.<\/span><\/p>\n

    Im Bereich der medizinischen KI-Bildgebung wurden zahlreiche Studien durchgef\u00fchrt, darunter wichtige Durchbr\u00fcche bei <\/span>Brustkrebs-Screening<\/span><\/a> und KI-Diagnostik, die <\/span>Spiel<\/span><\/a> oder sogar <\/span>\u00c4rzte schlagen<\/span><\/a> bei der Interpretation von Bildern.\u00a0<\/span><\/p>\n

    Mit Blick auf die Zukunft plant das Forschungsteam, die Verwendung flexiblerer Kontexts\u00e4tze zu untersuchen, die m\u00f6glicherweise Text oder mehrere Arten von Bildern enthalten.\u00a0<\/span><\/p>\n

    Au\u00dferdem wollen sie M\u00f6glichkeiten entwickeln, um die schlechtesten Vorhersagen von Tyche zu verbessern und das System in die Lage zu versetzen, die besten Segmentierungskandidaten zu empfehlen.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Die medizinische Bildgebung ist ein komplexer Bereich, in dem die Interpretation der Ergebnisse schwierig sein kann. KI-Modelle k\u00f6nnen \u00c4rzte bei der Analyse von Bildern unterst\u00fctzen, die auf krankheitsrelevante Anomalien hinweisen k\u00f6nnten. Die Sache hat jedoch einen Haken: Diese KI-Modelle liefern in der Regel nur eine einzige L\u00f6sung, w\u00e4hrend medizinische Bilder in der Realit\u00e4t oft mehrere Interpretationen zulassen. Wenn man f\u00fcnf Experten bittet, einen interessanten Bereich zu skizzieren, z. B. einen kleinen Knoten in einem Lungenscan, k\u00f6nnte man f\u00fcnf verschiedene Zeichnungen erhalten, da jeder von ihnen seine eigene Meinung dazu hat, wo der Knoten beginnt und wo er endet. Um dieses Problem zu l\u00f6sen, haben Forscher des MIT, der<\/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\/de\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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\/de\/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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