Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep‐learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related t...

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Published inJournal of magnetic resonance imaging Vol. 49; no. 4; pp. 939 - 954
Main Authors Mazurowski, Maciej A., Buda, Mateusz, Saha, Ashirbani, Bashir, Mustafa R.
Format Journal Article
LanguageEnglish
Published United States Wiley Subscription Services, Inc 01.04.2019
Subjects
Algorithms
Artificial Intelligence
Artificial neural networks
convolutional neural networks
Data processing
Deep Learning
Diagnostic Tests, Routine
Human performance
Humans
Image Processing, Computer-Assisted
Learning algorithms
Machine Learning
Magnetic Resonance Imaging
Medical imaging
Neural networks
Neural Networks, Computer
Radiography
Radiology
Radiology - methods
deep learning
convolutional neural networks
machine learning
radiology
artificial intelligence
medical imaging
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Abstract Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep‐learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep‐learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep‐learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939–954.
AbstractList Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep‐learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep‐learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep‐learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939–954.
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep-learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939-954.
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep learning algorithms. We also introduce basic concepts of deep learning including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that that they attempt to solve and review a broad range of deep learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep‐learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep‐learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep‐learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future.Level of Evidence: 3Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2019;49:939–954.
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep-learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939-954.Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep-learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep-learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939-954.
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep‐learning algorithms have shown groundbreaking performance in a variety of sophisticated tasks, especially those related to images. They have often matched or exceeded human performance. Since the medical field of radiology mainly relies on extracting useful information from images, it is a very natural application area for deep learning, and research in this area has rapidly grown in recent years. In this article, we discuss the general context of radiology and opportunities for application of deep‐learning algorithms. We also introduce basic concepts of deep learning, including convolutional neural networks. Then, we present a survey of the research in deep learning applied to radiology. We organize the studies by the types of specific tasks that they attempt to solve and review a broad range of deep‐learning algorithms being utilized. Finally, we briefly discuss opportunities and challenges for incorporating deep learning in the radiology practice of the future. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:939–954.
Author Bashir, Mustafa R.
Buda, Mateusz
Saha, Ashirbani
Mazurowski, Maciej A.
AuthorAffiliation 1. Department of Radiology, Duke University, Durham, NC
2. Department of Electrical and Computer Engineering, Duke University, Durham, NC
4. Center for Advanced Magnetic Resonance Development, Duke University, Durham, NC
3. Duke Medical Physics Program, Duke University, Durham, NC
AuthorAffiliation_xml – name: 2. Department of Electrical and Computer Engineering, Duke University, Durham, NC
– name: 3. Duke Medical Physics Program, Duke University, Durham, NC
– name: 1. Department of Radiology, Duke University, Durham, NC
– name: 4. Center for Advanced Magnetic Resonance Development, Duke University, Durham, NC
Author_xml – sequence: 1
  givenname: Maciej A.
  surname: Mazurowski
  fullname: Mazurowski, Maciej A.
  email: maciej.mazurowski@duke.edu
  organization: Duke University
– sequence: 2
  givenname: Mateusz
  surname: Buda
  fullname: Buda, Mateusz
  organization: Duke University
– sequence: 3
  givenname: Ashirbani
  orcidid: 0000-0002-7650-1720
  surname: Saha
  fullname: Saha, Ashirbani
  organization: Duke University
– sequence: 4
  givenname: Mustafa R.
  surname: Bashir
  fullname: Bashir, Mustafa R.
  organization: Duke University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30575178$$D View this record in MEDLINE/PubMed
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medical imaging
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SecondaryResourceType review_article
Snippet Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve...
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pubmed
crossref
wiley
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StartPage 939
SubjectTerms Algorithms
Artificial Intelligence
Artificial neural networks
convolutional neural networks
Data processing
Deep Learning
Diagnostic Tests, Routine
Human performance
Humans
Image Processing, Computer-Assisted
Learning algorithms
Machine Learning
Magnetic Resonance Imaging
Medical imaging
Neural networks
Neural Networks, Computer
Radiography
Radiology
Radiology - methods
Title Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fjmri.26534
https://www.ncbi.nlm.nih.gov/pubmed/30575178
https://www.proquest.com/docview/2190830312
https://www.proquest.com/docview/2159982467
https://pubmed.ncbi.nlm.nih.gov/PMC6483404
Volume 49
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