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[Neural network: A future in pathology?] / Intelligence artificielle : quel avenir en anatomie pathologique ?
Zemouri, Ryad; Devalland, Christine; Valmary-Degano, Séverine; Zerhouni, Noureddine.
  • Zemouri R; CEDRIC laboratory of the Conservatoire national des arts et métiers (CNAM), HESAM université, 292, rue Saint-Martin, 750141 Paris cedex 03, France. Electronic address: ryad.zemouri@cnam.fraa.
  • Devalland C; Service d'anatomie et cytologie pathologiques, hôpital nord Franche-Comté, 100, route de Moval, 90400 Trevenans, France. Electronic address: Christine.devalland@hnfc.fr.
  • Valmary-Degano S; TSA10217, service d'anatomie et cytologie pathologiques, CHU de Grenoble-Alpes, 38043 Grenoble cedex, France. Electronic address: svalmarydegano@chu-grenoble.fr.
  • Zerhouni N; ENSMM, CNR, FEMTO-ST institute, université de Bourgogne Franche-Comté, 25000 Besançon, France. Electronic address: zerhouni@ens2m.fr.
Ann Pathol ; 39(2): 119-129, 2019 Apr.
Article en Fr | MEDLINE | ID: mdl-30773224
ABSTRACT
Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models. Two phases are required a learning and a using phase. The two main applications are classification and regression Computer tools such as GPU computational accelerators or some development tools such as MATLAB libraries are used. Their application field is vast and allows the management of big data in genomics and molecular biology as well as the automated analysis of histological slides. The Whole Slide Image scanner can acquire and store slides in the form of digital images. This scanning associated with deep learning algorithms allows automatic recognition of lesions through the automatic recognition of regions of interest previously validated by the pathologist. These computer aided diagnosis techniques are tested in particular in mammary pathology and dermatopathology. They will allow an efficient and a more comprehensive vision, and will provide diagnosis assistance in pathology by correlating several biomedical data such as clinical, radiological and molecular biology data.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Patología / Inteligencia Artificial / Redes Neurales de la Computación Tipo de estudio: Prognostic_studies Límite: Humans Idioma: Fr Año: 2019 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Patología / Inteligencia Artificial / Redes Neurales de la Computación Tipo de estudio: Prognostic_studies Límite: Humans Idioma: Fr Año: 2019 Tipo del documento: Article