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Recent advances and clinical applications of deep learning in medical image analysis | |
2022-07 | |
发表期刊 | MEDICAL IMAGE ANALYSIS
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ISSN | 1361-8415 |
EISSN | 1361-8423 |
卷号 | 79 |
发表状态 | 已发表 |
DOI | 10.1016/j.media.2022.102444 |
摘要 | Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss major technical challenges and suggest possible solutions in the future research efforts. © 2022 |
关键词 | Deep learning Diagnosis Image analysis Image classification Image enhancement Large dataset Medical imaging Supervised learning Attention Clinical application Deep learning Detection Medical image Medical image analysis Registration Segmentation Self-supervised learning Vision transformer |
URL | 查看原文 |
收录类别 | SCI ; SCIE ; EI |
语种 | 英语 |
资助项目 | National Institute of General Medical Sciences, National Institutes of Health[P30CA225520] ; National Cancer Institute Cancer Center Support Grant[P20GM135009] |
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:000806232300009 |
出版者 | Elsevier B.V. |
EI入藏号 | 20221912082541 |
EI主题词 | Image segmentation |
EI分类号 | 461.1 Biomedical Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 461.6 Medicine and Pharmacology ; 723.2 Data Processing and Image Processing ; 746 Imaging Techniques |
原始文献类型 | Journal article (JA) |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/180933 |
专题 | 信息科学与技术学院_硕士生 |
通讯作者 | Qiu, Yuchen |
作者单位 | 1.Univ Oklahoma, Sch Elect & Comp Engn, Norman, OK 73019 USA 2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 3.Univ Oklahoma, Dept Pathol, Hlth Sci Ctr, Oklahoma City, OK 73104 USA 4.Univ Oklahoma, Dept Radiol, Hlth Sci Ctr, Oklahoma City, OK 73104 USA 5.Univ Oklahoma, Dept Obstet & Gynecol, Hlth Sci Ctr, Oklahoma City, OK 73104 USA |
推荐引用方式 GB/T 7714 | Chen, Xuxin,Wang, Ximin,Zhang, Ke,et al. Recent advances and clinical applications of deep learning in medical image analysis[J]. MEDICAL IMAGE ANALYSIS,2022,79. |
APA | Chen, Xuxin.,Wang, Ximin.,Zhang, Ke.,Fung, Kar-Ming.,Thai, Theresa C..,...&Qiu, Yuchen.(2022).Recent advances and clinical applications of deep learning in medical image analysis.MEDICAL IMAGE ANALYSIS,79. |
MLA | Chen, Xuxin,et al."Recent advances and clinical applications of deep learning in medical image analysis".MEDICAL IMAGE ANALYSIS 79(2022). |
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