Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions

被引：53
作者：
Choi H. [1 ]
机构：
[1] Cheonan Public Health Center, 234-1 Buldang-Dong, Seobuk-Gu, Cheonan
来源：
Nuclear Medicine and Molecular Imaging | 2018年 / 52卷 / 2期
关键词：
Convolutional neural network; Deep learning; Machine learning; Molecular imaging; Precision medicine;
D O I：
10.1007/s13139-017-0504-7
中图分类号：
学科分类号：
摘要：
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. © 2017, Korean Society of Nuclear Medicine.
引用
收藏
页码：109 / 118
页数：9
相关论文
共 73 条
[21]  
Oquab M., Bottou L., Laptev I., Sivic J., editors. Learning and transferring mid-level image representations using convolutional neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2014)
[22]  
LeCun Y., Bottou L., Bengio Y., Haffner P., Gradient-based learning applied to document recognition, Proc IEEE, 86, pp. 2278-2324, (1998)
[23]  
Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D et al., editors. Going deeper with convolutions, Proceedings of the IEEE conference on computer vision and pattern recognition, (2015)
[24]  
He K., Zhang X., Ren S., Sun J., editors. Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016)
[25]  
Vinyals O., Toshev A., Bengio S., Erhan D., editors. Show and tell: a neural image caption generator, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2015)
[26]  
Girshick R., editor. Fast r-cnn, Proceedings of the IEEE International Conference on Computer Vision, (2015)
[27]  
Ren S., He K., Girshick R., Sun J., Faster R.-C.N.N., Towards real-time object detection with region proposal networks, Adv Neural Inf Proces Syst, 28, pp. 91-99, (2015)
[28]  
Chen L.-C., Papandreou G., Kokkinos I., Murphy K., Yuille A.L., Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, arXiv:160600915, (2016)
[29]  
Badrinarayanan V., Kendall A., Cipolla R., Segnet: a deep convolutional encoder-decoder architecture for image segmentation, arXiv:151100561, (2015)
[30]  
Elmore J.G., Longton G.M., Carney P.A., Geller B.M., Onega T., Tosteson A.N., Et al., Diagnostic concordance among pathologists interpreting breast biopsy specimens, JAMA, 313, pp. 1122-1132, (2015)
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