Deep Learning in Medical Imaging

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 g...

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Veröffentlicht in:Neurospine Jg. 16; H. 4; S. 657 - 668
Hauptverfasser: Kim, Mingyu, Yun, Jihye, Cho, Yongwon, Shin, Keewon, Jang, Ryoungwoo, Bae, Hyun-jin, Kim, Namkug
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Korea (South) Korean Spinal Neurosurgery Society 01.12.2019
대한척추신경외과학회
Schlagworte:
artificial intelligence
deep learning
machine learning
precision medicine
radiology
Review
신경외과학
Deep learning
Radiology
Precision medicine
Artificial intelligence
Machine learning
ISSN:2586-6583, 2586-6591
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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https://doi.org/10.14245/ns.1938396.198
ISSN:2586-6583
2586-6591
DOI:10.14245/ns.1938396.198