Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications.

Techniques from the field of artificial intelligence, and more specifically machine (deep) learning methods, have been core components of most recent developments in the field of medical imaging. They are already being exploited or are being considered to tackle most tasks, including image reconstru...

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Publicado en:European Journal of Nuclear Medicine & Molecular Imaging Vol. 46; no. 13; pp. 2630 - 2638
Autores principales: Visvikis, Dimitris, Cheze Le Rest, Catherine, Jaouen, Vincent, Hatt, Mathieu
Formato: Journal Article
Publicado: Springer Nature Dec2019
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications.
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          Visvikis, Dimitris
          Cheze Le Rest, Catherine
          Jaouen, Vincent
          Hatt, Mathieu
        affil: LaTIM, INSERM UMR 1101, IBRBS, Faculty of Medicine, Univ Brest, 22 avenue Camille Desmoulins, 29238, Brest, France
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      ab: Techniques from the field of artificial intelligence, and more specifically machine (deep) learning methods, have been core components of most recent developments in the field of medical imaging. They are already being exploited or are being considered to tackle most tasks, including image reconstruction, processing (denoising, segmentation), analysis and predictive modelling. In this review we introduce and define these key concepts and discuss how the techniques from this field can be applied to nuclear medicine imaging applications with a particular focus on radio(geno)mics.
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      doctype: Journal Article
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    language: English
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