{"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\/pt\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/","title":{"rendered":"Investigadores constroem o \"Tyche\" para lidar com a incerteza na imagiologia m\u00e9dica"},"content":{"rendered":"

A imagiologia m\u00e9dica \u00e9 um dom\u00ednio complexo em que a interpreta\u00e7\u00e3o dos resultados pode ser um desafio. <\/strong><\/p>\n

Os modelos de IA podem ajudar os m\u00e9dicos atrav\u00e9s da an\u00e1lise de imagens que possam indicar anomalias indicadoras de doen\u00e7a.<\/p>\n

No entanto, h\u00e1 um sen\u00e3o: estes modelos de IA apresentam normalmente uma \u00fanica solu\u00e7\u00e3o quando, na realidade, as imagens m\u00e9dicas t\u00eam frequentemente m\u00faltiplas interpreta\u00e7\u00f5es.<\/p>\n

Se pedir a cinco especialistas para delinearem uma \u00e1rea de interesse, como um pequeno n\u00f3dulo num exame aos pulm\u00f5es, pode acabar por obter cinco desenhos diferentes, uma vez que todos eles podem ter as suas pr\u00f3prias opini\u00f5es sobre onde come\u00e7a e acaba o n\u00f3dulo, por exemplo.<\/p>\n

Para resolver este problema, os investigadores do MIT, do Broad Institute of MIT Harvard e do Massachusetts General Hospital criaram o Tyche, um sistema de IA que aceita a ambiguidade na segmenta\u00e7\u00e3o de imagens m\u00e9dicas.<\/span><\/p>\n

A segmenta\u00e7\u00e3o envolve a etiquetagem de pix\u00e9is espec\u00edficos numa imagem m\u00e9dica que representam estruturas importantes, como \u00f3rg\u00e3os ou c\u00e9lulas.\u00a0<\/span><\/p>\n

Marianne Rakic, candidata a doutoramento em ci\u00eancias inform\u00e1ticas no MIT e principal autora do estudo <\/span>estudo<\/span><\/a>O Dr. Hahn, da Universidade de Lisboa, explica: \"Ter op\u00e7\u00f5es pode ajudar na tomada de decis\u00f5es. O simples facto de ver que existe incerteza numa imagem m\u00e9dica pode influenciar as decis\u00f5es de algu\u00e9m, pelo que \u00e9 importante ter em conta essa incerteza.\"<\/span><\/p>\n

Com o nome da deusa grega do acaso, Tyche gera v\u00e1rias segmenta\u00e7\u00f5es poss\u00edveis para uma \u00fanica imagem m\u00e9dica para capturar a ambiguidade.\u00a0<\/span><\/p>\n

Cada segmenta\u00e7\u00e3o destaca regi\u00f5es ligeiramente diferentes, permitindo aos utilizadores escolher a mais adequada \u00e0s suas necessidades.\u00a0<\/span><\/p>\n

Rakic conta <\/span>Not\u00edcias do MIT<\/span><\/a>\"A produ\u00e7\u00e3o de v\u00e1rios candidatos e a garantia de que s\u00e3o diferentes uns dos outros d\u00e1-lhe realmente uma vantagem\".<\/span><\/p>\n

Ent\u00e3o, como \u00e9 que o Tyche funciona? Vamos decomp\u00f4-lo em quatro passos simples:<\/span><\/p>\n

    \n
  1. Aprender com o exemplo<\/b>: Os utilizadores fornecem ao Tyche um pequeno conjunto de imagens de exemplo, denominado \"conjunto de contexto\", que mostra a tarefa de segmenta\u00e7\u00e3o que pretendem executar. Estes exemplos podem incluir imagens segmentadas por diferentes especialistas humanos, ajudando o modelo a compreender a tarefa e o potencial de ambiguidade.<\/span><\/li>\n
  2. Ajustes na rede neural<\/b>: Os investigadores modificaram uma arquitetura de rede neural padr\u00e3o para permitir ao Tyche lidar com a incerteza. Ajustaram as camadas da rede para que as segmenta\u00e7\u00f5es potenciais geradas em cada etapa pudessem \"comunicar\" entre si e com os exemplos do conjunto de contexto.<\/span><\/li>\n
  3. M\u00faltiplas possibilidades<\/b>: O Tyche foi concebido para produzir v\u00e1rias previs\u00f5es com base numa \u00fanica entrada de imagem m\u00e9dica e no conjunto de contexto.\u00a0<\/span><\/li>\n
  4. Recompensar a qualidade<\/b>: O processo de forma\u00e7\u00e3o foi ajustado para recompensar o Tyche por produzir a melhor previs\u00e3o poss\u00edvel. Se o utilizador pedir cinco previs\u00f5es, pode ver todas as cinco segmenta\u00e7\u00f5es de imagens m\u00e9dicas produzidas pelo Tyche, mesmo que uma possa ser melhor.\u00a0<\/span><\/li>\n<\/ol>\n
    \"Imagiologia
    Na parte superior, os anotadores humanos mostram varia\u00e7\u00f5es na segmenta\u00e7\u00e3o de resultados de imagens m\u00e9dicas, uma vez que existem m\u00faltiplas interpreta\u00e7\u00f5es. As t\u00e9cnicas automatizadas tradicionais (a meio) s\u00e3o geralmente concebidas para tarefas espec\u00edficas, gerando uma \u00fanica segmenta\u00e7\u00e3o por imagem. Em contraste, o Tyche (em baixo) capta habilmente a gama de discord\u00e2ncias dos anotadores em v\u00e1rias modalidades e estruturas anat\u00f3micas, eliminando a necessidade de reciclagem ou ajustes. Fonte: ArXiv<\/a>.<\/figcaption><\/figure>\n

    Um dos maiores pontos fortes do Tyche \u00e9 a sua adaptabilidade. Pode assumir novas tarefas de segmenta\u00e7\u00e3o sem necessitar de ser novamente treinado de raiz.\u00a0<\/span><\/p>\n

    Normalmente, os modelos de IA para segmenta\u00e7\u00e3o de imagens m\u00e9dicas utilizam redes neuronais que requerem uma forma\u00e7\u00e3o extensiva em grandes conjuntos de dados e conhecimentos de aprendizagem autom\u00e1tica.\u00a0<\/span><\/p>\n

    Em contrapartida, o Tyche pode ser utilizado \"fora da caixa\" para v\u00e1rias tarefas, desde a dete\u00e7\u00e3o de les\u00f5es pulmonares em radiografias at\u00e9 \u00e0 identifica\u00e7\u00e3o de anomalias cerebrais em resson\u00e2ncias magn\u00e9ticas.<\/span><\/p>\n

    Foram realizados numerosos estudos no dom\u00ednio da imagiologia m\u00e9dica com IA, incluindo grandes avan\u00e7os em <\/span>rastreio do cancro da mama<\/span><\/a> e diagn\u00f3sticos de IA que <\/span>jogo<\/span><\/a> ou mesmo <\/span>m\u00e9dicos de choque<\/span><\/a> na interpreta\u00e7\u00e3o de imagens.\u00a0<\/span><\/p>\n

    Olhando para o futuro, a equipa de investiga\u00e7\u00e3o planeia explorar a utiliza\u00e7\u00e3o de conjuntos de contextos mais flex\u00edveis, possivelmente incluindo texto ou v\u00e1rios tipos de imagens.\u00a0<\/span><\/p>\n

    Pretendem tamb\u00e9m desenvolver formas de melhorar as piores previs\u00f5es do Tyche e permitir que o sistema recomende os melhores candidatos \u00e0 segmenta\u00e7\u00e3o.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    A imagiologia m\u00e9dica \u00e9 um dom\u00ednio complexo em que a interpreta\u00e7\u00e3o dos resultados pode ser um desafio. Os modelos de IA podem ajudar os m\u00e9dicos a analisar imagens que possam indicar anomalias que indiquem doen\u00e7as. No entanto, h\u00e1 um sen\u00e3o: estes modelos de IA apresentam normalmente uma \u00fanica solu\u00e7\u00e3o quando, na realidade, as imagens m\u00e9dicas t\u00eam muitas vezes m\u00faltiplas interpreta\u00e7\u00f5es. Se pedirmos a cinco especialistas para delinearem uma \u00e1rea de interesse, como um pequeno n\u00f3dulo num exame aos pulm\u00f5es, podemos acabar com cinco desenhos diferentes, uma vez que todos eles podem ter as suas pr\u00f3prias opini\u00f5es sobre onde come\u00e7a e acaba o n\u00f3dulo, por exemplo. Para resolver este problema, investigadores do MIT, do<\/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\/pt\/2024\/04\/researchers-build-tyche-to-embrace-uncertainty-in-medical-imaging\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\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\/pt\/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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