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Published： 16 March 2022 ，
Accepted： 12 January 2022
移动端阅览
Xi Jiang, Yixuan Yuan, Yaping Wang, Zhenxiang Xiao, Meilu Zhu, Zehua Chen, Tianming Liu, Dinggang Shen. A 20-year retrospect and prospect of medical imaging artificial intelligence in China. [J]. Journal of Image and Graphics 27(3):655-671(2022)
在过去20年里，医学影像技术、人工智能技术以及这两项技术相结合的临床应用都取得了长足发展。中国在该领域的研究也取得卓越成就，并且在全世界范围内的贡献比例仍在逐步提高。为了记录和总结国内同行的科研成果，本文对中国医学影像人工智能过去20年的发展历程进行回顾和展望。重点分析了国内同行在公认的医学影像人工智能领域的国际顶级刊物Medical Image Analysis(MedIA)和IEEE Transactions on Medical Imaging(TMI)以及顶级会议Medical Image Computing and Computer Assisted Intervention(MICCAI)发表的论文，定量统计了论文发表数量、作者身份、发表单位、作者合作链、关键词和被引次数等信息。同时总结了近20年中国医学影像人工智能发展进程中的重要事件，包括举办的医学影像人工智能知名国际和国内会议、《中国医学影像AI白皮书》的发布以及国内同行在COVID-19(corona virus disease 2019)期间的贡献，最后展望了中国医学影像人工智能领域未来的发展趋势。上述统计结果系统性地反映了在过去20年里中国在医学影像人工智能领域所取得的突出成绩。许多研究论文的作者将数据和源代码公开给全世界共享，为全世界医学影像人工智能的科研和教学做出了杰出贡献。通过本文中国医学影像人工智能领域的发展历程，可为医学影像人工智能同行，尤其为新一代的学者和学生提供科研和教学参考，也为继续促进和加强国际合作交流，为全世界该领域进一步的蓬勃发展做出重要贡献。
The development of medical imaging
artificial intelligence (AI) and clinical applications derived from AI-based medical imaging has been recognized in past two decades. The improvement and optimization of AI-based technologies have been significantly applied to various of clinical scenarios to strengthen the capability and accuracy of diagnosis and treatment. Nowadays
China has been playing a major role and making increasing contributions in the field of AI-based medical imaging. More worldwide researchers in the context of AI-based medical imaging have contributed to universities and institutions in China. The number of research papers published by Chinese scholars in top international journals and conferences like AI-based medical imaging has dramatically increased annually. Some AI-based medical imaging international conferences and summits have been successfully held in China. There is an increasing number of traditional medical
internet technology and AI enterprises contributing to the research and development of AI-based medical imaging products. More collaborative medical research projects have been implemented for AI-based medical imaging. The Chinese administrations have also planned relevant policies and issued strategic plans for AI-based medical imaging
and included the intelligent medical care as one of the key tasks for the development of new generation of AI in China in 2030. In order to review China's contribution for AI-based medical imaging
we conducted a 20 years review for AI-based medical imaging forecasting in China. Specifically
we summarized all papers published by Chinese scholars in the top AI-based medical imaging journals and conferences including Medical Image Analysis (MedIA)
IEEE Transactions on Medical Imaging (TMI)
and Medical Image Computing and Computer Assisted Intervention (MICCAI) in the past 20 years. The detailed quantitative metrics like the number of published papers
authorship
affiliations
author's cooperation network
keywords
and the number of citations were critically reviewed. Meanwhile
we briefly summarized some milestone events of AI-based medical imaging in China
including the renowned international and domestic conferences in AI-based medical imaging held in China
the release of the "The White Paper on Medical Imaging Artificial Intelligence in China"
as well as China's contributions during the COVID-19(corona virus desease 2019) pandemic. For instance
the total number of published papers in the past 20 years and the proportion of published papers in 2021 by Chinese affiliations have reached to 333 and 37.29% in MedIA
601 and 42.26% in TMI
and 985 and 44.26% in MICCAI. In those published papers by Chinese institutes
the proportion of the first and the corresponding Chinese authors is 71.97% in MedIA
69.64% in TMI
and 77.4% in MICCAI in 2021. The average number of citations per paper by Chinese institutes is 22
28
and 9 in MedIA
TMI
and MICCAI
respectively. In all published papers by Chinese institutes
the predominant research methods were transformed from conventional approaches to sparse representation in 2012
and to deep learning in 2017
which were close to the latest developmental trend of AI technologies. Besides conventional applications such as medical image registration
segmentation
reconstruction and computer-aided diagnosis
etc.
the published papers also focused on healthcare quick response in terms of COVID-19 pandemic. The China-derived data and source codes have been sharing in the global context to facilitate worldwide AI-based medical imaging research and education. Our analysis could provide a reference for international scientific research and education for newly Chinese scholars and students based on the growth of the global AI-based medical imaging. Finally
we promoted technology forecasting on AI-based medical imaging as mentioned below. First
strengthen the capability of deep learning for AI-based medical imaging further
including optimal and efficient deep learning
generalizable deep learning
explainable deep learning
fair deep learning
and responsible and trustworthy deep learning.Next
improve the availability and sharing of high-quality and benchmarked medical imaging datasets in the context of AI-based medical imaging development
validation
and dissemination are harnessed to reveal the key challenges in both basic scientific research and clinical applications. Third
focus on the multi-center and multi-modal medical imaging data acquisition and fusion
as well as integration with natural language such as diagnosis report. Fourth
awake doctors' intervention further to realize the clinical applications of AI-based medical imaging. Finally
conduct talent training
international collaboration
as well as sharing of open source data and codes for worldwide development of AI-based medical imaging.
医学影像人工智能(AI)发展历程国际合作定量统计
medical imagingartificial intelligence(AI)developmental historyinternational cooperationquantitative statistics
Chinese Innovative Alliance of Industry, Education, Research and Application of Artificial Intelligence for Medical Imaging. 2019. Releasing of the white paper on medical imaging artificial intelligence in China. Chinese Medical Sciences Journal, 34(2): #89 [DOI: 10.24920/003620]
Di D L, Shi F, Yan F H, Xia L M, Mo Z H, Ding Z X, Shan F, Song B, Li S R, Wei Y, Shao Y, Han M F, Gao Y Z, Sui H, Gao Y and Shen D G. 2021. Hypergraph learning for identification of COVID-19 with CT imaging. Medical Image Analysis, 68: #101910 [DOI: 10.1016/j.media.2020.101910]
Dong D, Tang Z C, Wang S, Hui H, Gong L X, Lu Y, Xue Z, Liao H E, Chen F, Yang F, Jin R H, Wang K, Liu Z Y, Wei J W, Mu W, Zhang H, Jiang J Y, Tian J and Li H J. 2021. The role of imaging in the detection and management of COVID-19: a review. IEEE Reviews in Biomedical Engineering, 14: 16-29 [DOI: 10.1109/RBME.2020.2990959]
Gao K, Su J P, Jiang Z B, Zeng L L, Feng Z C, Shen H, Rong P F, Xu X, Qin J, Yang Y X, Wang W and Hu D W. 2021. Dual-branch combination network (DCN): towards accurate diagnosis and lesion segmentation of COVID-19 using CT images. Medical Image Analysis, 67: #101836 [DOI: 10.1016/j.media.2020.101836]
Han Z Y, Wei B Z, Hong Y F, Li T Y, Cong J Y, Zhu X, Wei H F and Zhang W. 2020. Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning. IEEE Transactions on Medical Imaging, 39(8): 2584-2594 [DOI: 10.1109/TMI.2020.2996256]
Liu T M, Shen D G, Gao Y, Greenspan H, Niessen W, Rekik I, Schnabel J and Thrall J. 2021. Special issue on intelligent analysis of COVID-19 imaging data. Medical Image Analysis, 71: #102056 [DOI: 10.1016/j.media.2021.102056]
Ouyang X, Huo J Y, Xia L M, Shan F, Liu J, Mo Z H, Yan F H, Ding Z X, Yang Q, Song B, Shi F, Yuan H, Wei Y, Cao X H, Gao Y Z, Wu D J, Wang Q and Shen D G. 2020. Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Transactions on Medical Imaging, 39(8): 2595-2605 [DOI: 10.1109/TMI.2020.2995508]
Shen D G, Gao Y Z, Munoz-Barrutia A, Debuc D C and Percannella G. 2020. Guest editorial: special issue on imaging-based diagnosis of COVID-19. IEEE Transactions on Medical Imaging, 39(8): 2569-2571 [DOI: 10.1109/TMI.2020.3008025]
Shen D G, Wu G R and Suk H I. 2017. Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19: 221-248 [DOI: 10.1146/annurev-bioeng-071516-044442]
Shi F, Wang J, Shi J, Wu Z Y, Wang Q, Tang Z Y, He K L, Shi Y H and Shen D G. 2021. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 14: 4-15 [DOI: 10.1109/RBME.2020.2987975]
Wang J, Bao Y M, Wen Y F, Lu H B, Luo H, Xiang Y F, Li X M, Liu C and Qian D H. 2020. Prior-attention residual learning for more discriminative COVID-19 screening in CT images. IEEE Transactions on Medical Imaging, 39(8): 2572-2583 [DOI: 10.1109/TMI.2020.2994908]
Xue W F, Cao C Y, Liu J, Duan Y L, Cao H Y, Wang J, Tao X M, Chen Z J, Wu M, Zhang J X, Sun H, Jin Y, Yang X, Huang R B, Xiang F X, Song Y, You M J, Zhang W, Jiang L L, Zhang Z M, Kong S S, Tian Y, Zhang L, Ni D and Xie M X. 2021. Modality alignment contrastive learning for severity assessment of COVID-19 from lung ultrasound and clinical information. Medical Image Analysis, 69: #101975 [DOI: 10.1016/j.media.2021.101975]
Yang D, Xu Z Y, Li W Q, Myronenko A, Roth H R, Harmon S, Xu S, Turkbey B, Turkbey E, Wang X S, Zhu W T, Carrafiello G, Patella F, Cariati M, Obinata H, Mori H, Tamura K, An P, Wood B J and Xu D G. 2021a. Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan. Medical Image Analysis, 70: #101992 [DOI: 10.1016/j.media.2021.101992]
Yang J C, Shi R and Ni B B. 2021b. MedMNIST classification decathlon: a lightweight autoML benchmark for medical image analysis//Proceedings of the 18th IEEE International Symposium on Biomedical Imaging. Nice, France: IEEE: 191-195 [DOI: 10.1109/ISBI48211.2021.9434062http://dx.doi.org/10.1109/ISBI48211.2021.9434062]
Zhang K, Liu X H, Shen J, Li Z H, Sang Y, Wu X W, Zha Y F, Liang W H, Wang C D, Wang K, Ye L S, Gao M, Zhou Z G, Li L, WangJ, Yang Z H, Cai H M, Xu J, Yang L, Cai W J, Xu W Q, Wu S X, Zhang W, Jiang S P, Zheng L H, Zhang X, Wang L, Lu L, Li J M, Yin H P, Wang W, Li O L, Zhang C, Liang L, Wu T, Deng R Y, Wei K, Zhou Y, Chen T, Lau J Y N, Fok M, He J X, Lin T X, Li W M and Wang G Y. 2020. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell, 181(6): 1423-1433. E11 [DOI: 10.1016/j.cell.2020.04.045]
Zhong A X, Li X, Wu D F, Ren H, Kim K, Kim Y, Buch V, Neumark N, Bizzo B, Tak W Y, Park S Y, Lee Y R, Kang M K, Park J G, Kim B S, Chung W J, Guo N, Dayan I, Kalra M K and Li Q Z. 2021. Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19. Medical Image Analysis, 70: #101993 [DOI: 10.1016/j.media.2021.101993]
Zhu X F, Song B, Shi F, Chen Y B, Hu R Y, Gan J Z, Zhang W H, Li M, Wang L Y, Gao Y Z, Shan F and Shen D G. 2021. Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan. Medical Image Analysis, 67: #101824 [DOI: 10.1016/j.media.2020.101824]
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