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- Berkman Sahiner
- DIDSR/OSEL/CDRH U.S. Food and Drug Administration Silver Spring MD 20993 USA
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- Aria Pezeshk
- DIDSR/OSEL/CDRH U.S. Food and Drug Administration Silver Spring MD 20993 USA
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- Lubomir M. Hadjiiski
- Department of Radiology University of Michigan Ann Arbor MI 48109 USA
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- Xiaosong Wang
- Imaging Biomarkers and Computer‐aided Diagnosis Lab Radiology and Imaging Sciences NIH Clinical Center Bethesda MD 20892‐1182 USA
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- Karen Drukker
- Department of Radiology University of Chicago Chicago IL 60637 USA
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- Kenny H. Cha
- DIDSR/OSEL/CDRH U.S. Food and Drug Administration Silver Spring MD 20993 USA
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- Ronald M. Summers
- Imaging Biomarkers and Computer‐aided Diagnosis Lab Radiology and Imaging Sciences NIH Clinical Center Bethesda MD 20892‐1182 USA
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- Maryellen L. Giger
- Department of Radiology University of Chicago Chicago IL 60637 USA
説明
<jats:p>The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.</jats:p>
収録刊行物
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- Medical Physics
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Medical Physics 46 (1), e1-, 2018-11-20
Wiley
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詳細情報 詳細情報について
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- CRID
- 1362825894453585408
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- DOI
- 10.1002/mp.13264
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- ISSN
- 24734209
- 00942405
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- データソース種別
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- Crossref