{"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\/es\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","title":{"rendered":"Los investigadores construyen \"Tyche\" para hacer frente a la incertidumbre en la imagen m\u00e9dica"},"content":{"rendered":"

La imagen m\u00e9dica es un campo complejo en el que interpretar los resultados puede ser todo un reto. <\/strong><\/p>\n

Los modelos de inteligencia artificial pueden ayudar a los m\u00e9dicos analizando im\u00e1genes que podr\u00edan indicar anomal\u00edas causantes de enfermedades.<\/p>\n

Sin embargo, hay una trampa: estos modelos de IA suelen dar una \u00fanica soluci\u00f3n cuando, en realidad, las im\u00e1genes m\u00e9dicas suelen tener m\u00faltiples interpretaciones.<\/p>\n

Si pide a cinco expertos que esbocen un \u00e1rea de inter\u00e9s, como un peque\u00f1o bulto en un esc\u00e1ner pulmonar, podr\u00eda acabar con cinco dibujos diferentes, ya que todos podr\u00edan tener sus propias opiniones sobre d\u00f3nde empieza y acaba el bulto, por ejemplo.<\/p>\n

Para hacer frente a este problema, investigadores del MIT, el Instituto Broad del MIT de Harvard y el Hospital General de Massachusetts han creado Tyche, un sistema de IA que acepta la ambig\u00fcedad en la segmentaci\u00f3n de im\u00e1genes m\u00e9dicas.<\/span><\/p>\n

La segmentaci\u00f3n consiste en etiquetar p\u00edxeles espec\u00edficos de una imagen m\u00e9dica que representan estructuras importantes, como \u00f3rganos o c\u00e9lulas.\u00a0<\/span><\/p>\n

Marianne Rakic, candidata al doctorado en inform\u00e1tica del MIT y autora principal del <\/span>estudiar<\/span><\/a>explica: \"Tener opciones puede ayudar en la toma de decisiones. Incluso el mero hecho de ver que hay incertidumbre en una imagen m\u00e9dica puede influir en las decisiones de alguien, por lo que es importante tener en cuenta esta incertidumbre.\"<\/span><\/p>\n

Tyche, que recibe su nombre de la diosa griega del azar, genera m\u00faltiples segmentaciones posibles para una sola imagen m\u00e9dica con el fin de captar la ambig\u00fcedad.\u00a0<\/span><\/p>\n

Cada segmentaci\u00f3n destaca regiones ligeramente diferentes, lo que permite a los usuarios elegir la m\u00e1s adecuada a sus necesidades.\u00a0<\/span><\/p>\n

Rakic dice <\/span>Noticias del MIT<\/span><\/a>\"Dar salida a varios candidatos y asegurarse de que son diferentes entre s\u00ed realmente te da una ventaja\".<\/span><\/p>\n

\u00bfC\u00f3mo funciona Tyche? Desglos\u00e9moslo en cuatro sencillos pasos:<\/span><\/p>\n

    \n
  1. Aprender con el ejemplo<\/b>: Los usuarios proporcionan a Tyche un peque\u00f1o conjunto de im\u00e1genes de ejemplo, denominado \"conjunto de contexto\", que muestran la tarea de segmentaci\u00f3n que desean realizar. Estos ejemplos pueden incluir im\u00e1genes segmentadas por diferentes expertos humanos, lo que ayuda al modelo a comprender la tarea y las posibles ambig\u00fcedades.<\/span><\/li>\n
  2. Ajustes de la red neuronal<\/b>: Los investigadores modificaron una arquitectura de red neuronal est\u00e1ndar para que Tyche pudiera manejar la incertidumbre. Ajustaron las capas de la red para que las segmentaciones potenciales generadas en cada paso pudieran \"comunicarse\" entre s\u00ed y con los ejemplos del conjunto contextual.<\/span><\/li>\n
  3. M\u00faltiples posibilidades<\/b>: Tyche est\u00e1 dise\u00f1ado para generar m\u00faltiples predicciones basadas en una \u00fanica imagen m\u00e9dica de entrada y el conjunto de contextos.\u00a0<\/span><\/li>\n
  4. Recompensar la calidad<\/b>: El proceso de entrenamiento se ha modificado para recompensar a Tyche por producir la mejor predicci\u00f3n posible. Si el usuario pide cinco predicciones, puede ver las cinco segmentaciones de im\u00e1genes m\u00e9dicas producidas por Tyche, aunque una sea mejor.\u00a0<\/span><\/li>\n<\/ol>\n
    \"Imagen
    En la parte superior, los anotadores humanos muestran variaciones en la segmentaci\u00f3n de resultados de im\u00e1genes m\u00e9dicas, ya que existen m\u00faltiples interpretaciones. Las t\u00e9cnicas automatizadas tradicionales (centro) suelen estar dise\u00f1adas para tareas espec\u00edficas y generan una \u00fanica segmentaci\u00f3n por imagen. En cambio, Tyche (abajo) capta h\u00e1bilmente la gama de desacuerdos de los anotadores en varias modalidades y estructuras anat\u00f3micas, eliminando la necesidad de reentrenamientos o ajustes. Fuente: ArXiv<\/a>.<\/figcaption><\/figure>\n

    Uno de los puntos fuertes de Tyche es su adaptabilidad. Puede asumir nuevas tareas de segmentaci\u00f3n sin necesidad de volver a formarse desde cero.\u00a0<\/span><\/p>\n

    Normalmente, los modelos de IA para la segmentaci\u00f3n de im\u00e1genes m\u00e9dicas utilizan redes neuronales que requieren un amplio entrenamiento en grandes conjuntos de datos y experiencia en aprendizaje autom\u00e1tico.\u00a0<\/span><\/p>\n

    En cambio, Tyche puede utilizarse \"fuera de la caja\" para diversas tareas, desde detectar lesiones pulmonares en radiograf\u00edas hasta identificar anomal\u00edas cerebrales en resonancias magn\u00e9ticas.<\/span><\/p>\n

    Se han llevado a cabo numerosos estudios en el campo de la imagen m\u00e9dica con IA, entre los que se incluyen importantes avances en <\/span>cribado del c\u00e1ncer de mama<\/span><\/a> y diagn\u00f3sticos de IA que <\/span>match<\/span><\/a> o incluso <\/span>m\u00e9dicos golpeadores<\/span><\/a> en la interpretaci\u00f3n de im\u00e1genes.\u00a0<\/span><\/p>\n

    De cara al futuro, el equipo de investigaci\u00f3n tiene previsto explorar el uso de conjuntos de contextos m\u00e1s flexibles, que posiblemente incluyan texto o varios tipos de im\u00e1genes.\u00a0<\/span><\/p>\n

    Tambi\u00e9n quieren desarrollar formas de mejorar las peores predicciones de Tyche y permitir que el sistema recomiende los mejores candidatos de segmentaci\u00f3n.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    El diagn\u00f3stico m\u00e9dico por imagen es un campo complejo en el que interpretar los resultados puede resultar complicado. Los modelos de inteligencia artificial pueden ayudar a los m\u00e9dicos analizando im\u00e1genes que podr\u00edan indicar anomal\u00edas patol\u00f3gicas. Sin embargo, hay un truco: estos modelos de IA suelen dar una \u00fanica soluci\u00f3n cuando, en realidad, las im\u00e1genes m\u00e9dicas suelen tener m\u00faltiples interpretaciones. Si se pide a cinco expertos que dibujen un \u00e1rea de inter\u00e9s, como un peque\u00f1o bulto en un esc\u00e1ner pulmonar, es posible que se obtengan cinco dibujos distintos, ya que cada uno podr\u00eda tener su propia opini\u00f3n sobre d\u00f3nde empieza y acaba el bulto, por ejemplo. Para resolver este problema, investigadores del MIT, el<\/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\/es\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\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\/es\/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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