Neurospine.
2019 Dec;16(4):657-668. 10.14245/ns.1938396.198.
Deep Learning in Medical Imaging
- Affiliations
-
- 1Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea. namkugkim@gmail.com
- 2Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
- KMID: 2469039
- DOI: http://doi.org/10.14245/ns.1938396.198
Abstract
- The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learnable data, ANN has suffered from overfitting and vanishing gradient problems for training deep networks. The advancement of computing power with graphics processing units and the availability of large data acquisition, deep neural network outperforms human or other ML capabilities in computer vision and speech recognition tasks. These potentials are recently applied to healthcare problems, including computer-aided detection/diagnosis, disease prediction, image segmentation, image generation, etc. In this review article, we will explain the history, development, and applications in medical imaging
- Full Text Links
-
- Actions
-
Cited
- CITED
-
- Close
- Share
-
- Similar articles
-
- Current status of deep learning applications in abdominal ultrasonography
- Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions
- Development of an Optimized Deep Learning Model for Medical Imaging
- Deep-Learning-Based Molecular Imaging Biomarkers: Toward Data-Driven Theranostics
- Deep Learning in MR Motion Correction:a Brief Review and a New Motion Simulation Tool (view2Dmotion)
