Prospects of deep learning for medical imaging

被引:42
|
作者
Kim, Jonghoon [1 ,2 ]
Hong, Jisu [1 ,2 ]
Park, Hyunjin [2 ,3 ]
机构
[1] Sungkyunkwan Univ, Dept Elect Elect & Comp Engn, Suwon, South Korea
[2] IBS, Ctr Neurosci Imaging Res, Suwon, South Korea
[3] Sungkyunkwan Univ, Sch Elect & Elect Engn, 2066 Seobu Ro, Suwon 16419, South Korea
来源
PRECISION AND FUTURE MEDICINE | 2018年 / 2卷 / 02期
基金
新加坡国家研究基金会;
关键词
Deep learning; Diagnostic imaging; Machine learning;
D O I
10.23838/pfm.2018.00030
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research. First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. Third, well-known software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided.
引用
收藏
页码:37 / 52
页数:16
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