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

doi:10.1016/j.media.2023.102762

	Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
	Li, Jun 1; Chen, Junyu 2; Tang, Yucheng 3; Wang, Ce 1; Landman, Bennett A.3; Zhou, S. Kevin 1,4,5
	2023-04-01
发表期刊	MEDICAL IMAGE ANALYSIS
ISSN	1361-8415
卷号	85 页码:38
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.
关键词	Transformer Medical imaging Survey
DOI	10.1016/j.media.2023.102762
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000993037300001
出版者	ELSEVIER
引用统计	被引频次：45[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/2XEOYT63/21434
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.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, Peoples R China 2.Johns Hopkins Med Inst, Russell H Morgan Dept Radiol & Radiol Sci, Baltimore, MD USA 3.Vanderbilt Univ, Dept Elect & Comp Engn, Nashville, TN USA 4.Univ Sci & Technol China, Ctr Med Imaging Robot & Analyt Comp & Learning MIR, Sch Biomed Engn, Suzhou 215123, Peoples R China 5.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot & Analyt Comp & Learning MIR, Suzhou 215123, Peoples R China
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.