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A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
文献类型：期刊论文
| 作者 | Zhou, S. Kevin13,14; Greenspan, Hayit12; Davatzikos, Christos10,11; Duncan, James S.8,9; Van Ginneken, Bram7; Madabhushi, Anant5,6; Prince, Jerry L.4; Rueckert, Daniel2,3; Summers, Ronald M.1 |
| 刊名 | PROCEEDINGS OF THE IEEE
 |
| 出版日期 | 2021-05-01 |
| 卷号 | 109期号:5页码:820-838 |
| ISSN号 | 0018-9219 |
| 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 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000645896700010 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17733]  |
| 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.NIH, Clin Ctr, Bldg 10, Bethesda, MD 20892 USA 2.Imperial Coll London, Dept Comp, London SW7 2AZ, England 3.Tech Univ Munich TU Munich, Klinikum Rechts Isar, D-81675 Munich, Germany 4.Johns Hopkins Univ, Elect & Comp Engn Dept, Baltimore, MD 21218 USA 5.Louis Stokes Cleveland Vet Adm Med Ctr, Cleveland, OH 44106 USA 6.Case Western Reserve Univ, Dept Biomed Engn, Cleveland, OH 44106 USA 7.Radboud Univ Nijmegen Med Ctr, NL-6525 GA Nijmegen, Netherlands 8.Yale Univ, Dept Radiol & Biomed Imaging, New Haven, CT 06520 USA 9.Yale Univ, Dept Biomed Engn, New Haven, CT 06520 USA 10.Univ Penn, Elect & Syst Engn Dept, Philadelphia, PA 19104 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. |
入库方式： OAI收割
来源：计算技术研究所
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