A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

doi:10.1109/JPROC.2021.3054390

	A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
	Zhou, S. Kevin 1,2; Greenspan, Hayit 3; Davatzikos, Christos 4,5; Duncan, James S.6,7; Van Ginneken, Bram 8; Madabhushi, Anant 9,10; Prince, Jerry L.11; Rueckert, Daniel 12,13; Summers, Ronald M.14
	2021-05-01
发表期刊	PROCEEDINGS OF THE IEEE
ISSN	0018-9219
卷号	109 期号:5 页码:820-838
摘要	Since its renaissance, deep learning (DL) has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high-performance computing. However, medical imaging presents unique challenges that confront DL approaches. In this survey article, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in DL are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, and so on. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
关键词	Imaging Medical diagnostic imaging Image segmentation Diseases Task analysis Medical services Computed tomography Deep learning (DL) medical imaging survey
DOI	10.1109/JPROC.2021.3054390
收录类别	SCI
语种	英语
资助项目	National Institutes of Health[1U24CA199374-01] ; National Institutes of Health[R01CA202752-01A1] ; National Institutes of Health[R01CA208236-01A1] ; National Institutes of Health[R01CA216579-01A1] ; National Institutes of Health[R01CA220581-01A1] ; National Institutes of Health[1U01CA239055-01] ; National Institutes of Health[1U54CA254566-01] ; National Institutes of Health[1U01CA248226-01] ; National Institutes of Health[1R43EB028736-01] ; VA Merit Review Award from the Biomedical Laboratory Research and Development Service of the United States Department of Veterans Affairs[IBX004121A] ; Israeli Science Foundation (ISF) ; Ministry of Science Technology ; National Institutes of Health Clinical Center
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000645896700010
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：257[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17734
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, S. Kevin
作者单位	1.Univ Sci & Technol China, Sch Biomed Engn, Hefei 230052, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Tel Aviv Univ, Fac Engn, Dept Biomed Engn, IL-69978 Tel Aviv, Israel 4.Univ Penn, Radiol Dept, Philadelphia, PA 19104 USA 5.Univ Penn, Elect & Syst Engn Dept, Philadelphia, PA 19104 USA 6.Yale Univ, Dept Biomed Engn, New Haven, CT 06520 USA 7.Yale Univ, Dept Radiol & Biomed Imaging, New Haven, CT 06520 USA 8.Radboud Univ Nijmegen Med Ctr, NL-6525 GA Nijmegen, Netherlands 9.Case Western Reserve Univ, Dept Biomed Engn, Cleveland, OH 44106 USA 10.Louis Stokes Cleveland Vet Adm Med Ctr, Cleveland, OH 44106 USA 11.Johns Hopkins Univ, Elect & Comp Engn Dept, Baltimore, MD 21218 USA 12.Tech Univ Munich TU Munich, Klinikum Rechts Isar, D-81675 Munich, Germany 13.Imperial Coll London, Dept Comp, London SW7 2AZ, England 14.NIH, Clin Ctr, Bldg 10, Bethesda, MD 20892 USA
推荐引用方式 GB/T 7714	Zhou, S. Kevin,Greenspan, Hayit,Davatzikos, Christos,et al. A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises[J]. PROCEEDINGS OF THE IEEE,2021,109(5):820-838.
APA	Zhou, S. Kevin.,Greenspan, Hayit.,Davatzikos, Christos.,Duncan, James S..,Van Ginneken, Bram.,...&Summers, Ronald M..(2021).A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises.PROCEEDINGS OF THE IEEE,109(5),820-838.
MLA	Zhou, S. Kevin,et al."A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises".PROCEEDINGS OF THE IEEE 109.5(2021):820-838.