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Stud Health Technol Inform ; 316: 1110-1114, 2024 Aug 22.
Artigo em Inglês | MEDLINE | ID: mdl-39176576

RESUMO

Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in prostate carcinoma focusing on comparing traditional and recent architectures. A standardized image classification pipeline, based on the AUCMEDI framework, facilitated robust evaluation using an in-house dataset consisting of 34,264 annotated tissue tiles. The results indicated varying sensitivity across architectures, with ConvNeXt demonstrating the strongest performance. Notably, newer architectures achieved superior performance, even though with challenges in differentiating closely related Gleason grades. The ConvNeXt model was capable of learning a balance between complexity and generalizability. Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer.


Assuntos
Aprendizado Profundo , Gradação de Tumores , Neoplasias da Próstata , Masculino , Humanos , Neoplasias da Próstata/patologia , Redes Neurais de Computação , Interpretação de Imagem Assistida por Computador/métodos
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