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

被引：0
作者：
Dimitris Visvikis
Catherine Cheze Le Rest
Vincent Jaouen
Mathieu Hatt
机构：
[1] LaTIM,Nuclear Medicine Department
[2] INSERM UMR 1101,undefined
[3] IBRBS,undefined
[4] Faculty of Medicine,undefined
[5] Univ Brest,undefined
[6] CHU Milétrie,undefined
来源：
European Journal of Nuclear Medicine and Molecular Imaging | 2019年 / 46卷
关键词：
Artificial intelligence; Machine learning; Deep learning; Radiomics; Radiogenomics;
D O I：
暂无
中图分类号：
学科分类号：
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.
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收藏
页码：2630 / 2637
页数：7
相关论文
共 325 条
[1]  
Beyer T(2000)A combined PET/CT scanner for clinical oncology J Nucl Med 41 1369-1379
[2]  
Townsend DW(2017)Pushing the limits in time-of-flight PET imaging IEEE Trans Radiat Plasma Med Sci. 1 473-485
[3]  
Brun T(2018)Innovations in instrumentation for positron emission tomography Semin Nucl Med 48 311-331
[4]  
Kinahan PE(2017)Radiomics in PET/CT: more than meets the eye? J Nucl Med 58 365-366
[5]  
Charron M(2019)Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence Bus Horiz 62 15-25
[6]  
Roddy R(2016)Mastering the game of go with deep neural networks and tree search Nature 529 484-489
[7]  
Lecoq P(2018)Artificial intelligence in radiology Nat Rev Cancer 18 500-510
[8]  
Berg E(2003)An introduction to variable and feature selection J Mach Learn Res 3 1157-1182
[9]  
Cherry SR(2015)Machine learning methods for quantitative radiomic biomarkers Sci Rep 5 436-444
[10]  
Hatt M(2015)Deep learning Nature 521 85-117
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