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Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives

文献类型：期刊论文

作者Li, Jun4; Chen, Junyu5; Tang, Yucheng1; Wang, Ce4; Landman, Bennett A.1; Zhou, S. Kevin2,3,4
刊名MEDICAL IMAGE ANALYSIS
出版日期2023-04-01
卷号85页码:38
ISSN号1361-8415
关键词Transformer Medical imaging Survey
DOI10.1016/j.media.2023.102762
英文摘要Transformer, one of the latest technological advances of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear some resemblance to computer vision, it is natural to inquire about the status quo of Transformers in medical imaging and ask the question: can the Transformer models transform medical imaging? In this paper, we attempt to make a response to the inquiry. After a brief introduction of the fundamentals of Transformers, especially in comparison with convolutional neural networks (CNNs), and highlighting key defining properties that characterize the Transformers, we offer a comprehensive review of the state-of-the-art Transformer-based approaches for medical imaging and exhibit current research progresses made in the areas of medical image segmentation, recognition, detection, registration, reconstruction, enhancement, etc. In particular, what distinguishes our review lies in its organization based on the Transformer's key defining properties, which are mostly derived from comparing the Transformer and CNN, and its type of architecture, which specifies the manner in which the Transformer and CNN are combined, all helping the readers to best understand the rationale behind the reviewed approaches. We conclude with discussions of future perspectives.
资助项目National Natural Science Foundation of China (NSFC)[62271465] ; National Natural Science Foundation of China (NSFC)[U01-CA140204] ; National Natural Science Foundation of China (NSFC)[R01-EB031023] ; National Institutes of Health, USA
WOS研究方向Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者ELSEVIER
WOS记录号WOS:000993037300001
源URL[http://119.78.100.204/handle/2XEOYT63/21433]  
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.Vanderbilt Univ, Dept Elect & Comp Engn, Nashville, TN USA
2.Univ Sci & Technol China, Ctr Med Imaging Robot & Analyt Comp & Learning MIR, Sch Biomed Engn, Suzhou 215123, Peoples R China
3.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot & Analyt Comp & Learning MIR, Suzhou 215123, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, Peoples R China
5.Johns Hopkins Med Inst, Russell H Morgan Dept Radiol & Radiol Sci, Baltimore, MD USA
推荐引用方式
GB/T 7714		 Li, Jun,Chen, Junyu,Tang, Yucheng,et al. Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives[J]. MEDICAL IMAGE ANALYSIS,2023,85:38.
APA Li, Jun,Chen, Junyu,Tang, Yucheng,Wang, Ce,Landman, Bennett A.,&Zhou, S. Kevin.(2023).Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives.MEDICAL IMAGE ANALYSIS,85,38.
MLA Li, Jun,et al."Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives".MEDICAL IMAGE ANALYSIS 85(2023):38.

入库方式： OAI收割

来源：计算技术研究所

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