Deep Learning in Medical Image Analysis

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit h...

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Veröffentlicht in:	Annual review of biomedical engineering Jg. 19; S. 221 - 248
Hauptverfasser:	Shen, Dinggang, Wu, Guorong, Suk, Heung-Il
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 21.06.2017
Schlagworte:	Algorithms Diagnostic Imaging - methods Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Neural Networks (Computer) Pattern Recognition, Automated - methods Unsupervised Machine Learning unsupervised feature learning deep learning medical image analysis
ISSN:	1545-4274
Online-Zugang:	Weitere Angaben
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Abstract	This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
AbstractList	This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
Author	Wu, Guorong Shen, Dinggang Suk, Heung-Il
Author_xml	– sequence: 1 givenname: Dinggang surname: Shen fullname: Shen, Dinggang email: dgshen@med.unc.edu, hisuk@korea.ac.kr organization: Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea; email: hisuk@korea.ac.kr – sequence: 2 givenname: Guorong surname: Wu fullname: Wu, Guorong email: dgshen@med.unc.edu organization: Department of Radiology, University of North Carolina, Chapel Hill, North Carolina 27599; email: dgshen@med.unc.edu – sequence: 3 givenname: Heung-Il surname: Suk fullname: Suk, Heung-Il email: hisuk@korea.ac.kr organization: Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea; email: hisuk@korea.ac.kr
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28301734$$D View this record in MEDLINE/PubMed
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SubjectTerms	Algorithms Diagnostic Imaging - methods Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Neural Networks (Computer) Pattern Recognition, Automated - methods Unsupervised Machine Learning
Title	Deep Learning in Medical Image Analysis
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