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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한핵의학회 Nuclear medicine and molecular imaging : NMMI Nuclear medicine and molecular imaging : NMMI 제52권 제2호
- 발행연도
- 2018.1
- 수록면
- 109 - 118 (10page)
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Recent advances in deep learning have impacted various scientific and industrial fields. Due to the rapid application of deep learning in biomedical data, molecular imaging has also started to adopt this technique. In this regard, it is expected that deep learning will potentially affect the roles of molecular imaging experts as well as clinical decision making. This review firstly offers a basic overview of deep learning particularly for image data analysis to give knowledge to nuclear medicine physicians and researchers. Because of the unique characteristics and distinctive aims of various types of molecular imaging, deep learning applications can be different from other fields. In this context, the review deals with current perspectives of deep learning in molecular imaging particularly in terms of development of biomarkers. Finally, future challenges of deep learning application for molecular imaging and future roles of experts in molecular imaging will be discussed.
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참고문헌 (73)
참고문헌 신청
WeberGM / 2014 / Finding the missing link for big biomedical data / JAMA 311:2479~2480
Kuo M-H / 2014 / Health big data analytics: current perspectives, challenges and potential solutions / Int J Big Data Intell 1:114~126
Bengio Y / 2009 / Learning deep architectures for AI. Foundations and $trends^{(R)}$ in / Mach Learn. 2:1~127
LeCun Y / 2015 / Deep learning / Nature 521:436~444
Russakovsky O / 2015 / Imagenet large scale visual recognition challenge / Int J ComputVis 115:211~252
Kuo M-H / 2014 / Health big data analytics: current perspectives, challenges and potential solutions / Int J Big Data Intell 1:114~126
Bengio Y / 2009 / Learning deep architectures for AI. Foundations and
LeCun Y / 2015 / Deep learning / Nature 521:436~444
Russakovsky O / 2015 / Imagenet large scale visual recognition challenge / Int J ComputVis 115:211~252
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