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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Vydáno v:	Neurospine Ročník 16; číslo 4; s. 657 - 668
Hlavní autoři:	Kim, Mingyu, Yun, Jihye, Cho, Yongwon, Shin, Keewon, Jang, Ryoungwoo, Bae, Hyun-jin, Kim, Namkug
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Korea (South) Korean Spinal Neurosurgery Society 01.12.2019 대한척추신경외과학회
Témata:	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
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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.
AbstractList	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 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 KCI Citation Count: 27 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.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. 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.
Author	Jang, Ryoungwoo Cho, Yongwon Shin, Keewon Kim, Namkug Bae, Hyun-jin Kim, Mingyu Yun, Jihye
Author_xml	– sequence: 1 givenname: Mingyu surname: Kim fullname: Kim, Mingyu – sequence: 2 givenname: Jihye surname: Yun fullname: Yun, Jihye – sequence: 3 givenname: Yongwon surname: Cho fullname: Cho, Yongwon – sequence: 4 givenname: Keewon surname: Shin fullname: Shin, Keewon – sequence: 5 givenname: Ryoungwoo surname: Jang fullname: Jang, Ryoungwoo – sequence: 6 givenname: Hyun-jin surname: Bae fullname: Bae, Hyun-jin – sequence: 7 givenname: Namkug orcidid: 0000-0002-3438-2217 surname: Kim fullname: Kim, Namkug
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31905454$$D View this record in MEDLINE/PubMed https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002542239$$DAccess content in National Research Foundation of Korea (NRF)
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Title	Deep Learning in Medical Imaging
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