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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한자기공명의과학회 Investigative Magnetic Resonance Imaging Investigative Magnetic Resonance Imaging 제23권 제2호
- 발행연도
- 2019.1
- 수록면
- 81 - 99 (19page)
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Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.
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참고문헌 (115)
참고문헌 신청
Krizhevsky A / 2012 / ImageNet classification with deep convolutional neural networks / NIPS 2012: Neural Information Processing Systems :1097~1105
Hinton G / 2012 / Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups / IEEE Signal Processing Magazine 29:82~97
Sutskever I / 2014 / Sequence to sequence learning with neural networks / Proceedings of the 27th International Conference on Neural Information Processing Systems 2:3104~3112
Jifara W / 2017 / Medical image denoising using convolutional neural network: a residual learning approach / J Supercomput :1~15
Vincent P / 2010 / Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion / J Mach Learn Res 11:3371~3408
Hinton G / 2012 / Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups / IEEE Signal Processing Magazine 29:82~97
Sutskever I / 2014 / Sequence to sequence learning with neural networks / Proceedings of the 27th International Conference on Neural Information Processing Systems 2:3104~3112
Jifara W / 2017 / Medical image denoising using convolutional neural network: a residual learning approach / J Supercomput :1~15
Vincent P / 2010 / Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion / J Mach Learn Res 11:3371~3408
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