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European Journal of Nuclear Medicine and Molecular Imaging
Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging 13/2019

06.07.2019 | Review Article

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

verfasst von: Dimitris Visvikis, Catherine Cheze Le Rest, Vincent Jaouen, Mathieu Hatt

Erschienen in: European Journal of Nuclear Medicine and Molecular Imaging | Ausgabe 13/2019

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Abstract

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.
Fußnoten
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Metadaten
Titel
Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications
verfasst von
Dimitris Visvikis
Catherine Cheze Le Rest
Vincent Jaouen
Mathieu Hatt
Publikationsdatum
06.07.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
European Journal of Nuclear Medicine and Molecular Imaging / Ausgabe 13/2019
Print ISSN: 1619-7070
Elektronische ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-019-04373-w

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