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

被引：107
作者：
Visvikis, Dimitris [1 ]
Le Rest, Catherine Cheze [2 ]
Jaouen, Vincent [1 ]
Hatt, Mathieu [1 ]
机构：
[1] Univ Brest, Fac Med, IBRBS, LaTIM,INSERM UMR 1101, 22 Ave Camille Desmoulins, F-29238 Brest, France
[2] CHU Miletrie, Nucl Med Dept, Poitiers, France
来源：
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING | 2019年 / 46卷 / 13期
关键词：
Artificial intelligence; Machine learning; Deep learning; Radiomics; Radiogenomics; CONVOLUTIONAL NEURAL-NETWORKS; ATTENUATION CORRECTION; RADIOMICS; CANCER; SEGMENTATION; IMAGES; PET/CT; CLASSIFICATION; PREDICTION; FEATURES;
D O I：
10.1007/s00259-019-04373-w
中图分类号：
R8 [特种医学]; R445 [影像诊断学];
学科分类号：
1002 ; 100207 ; 1009 ;
摘要：
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.
引用
收藏
页码：2630 / 2637
页数：8
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