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Advances in Deep Learning-Based Medical Image Analysis. 基于深度学习的医学图像分析研究进展

Health data science Pub Date : 2021-05-19 eCollection Date: 2021-01-01 DOI: 10.34133/2021/8786793

Xiaoqing Liu, Kunlun Gao, Bo Liu, Chengwei Pan, Kongming Liang, Lifeng Yan, Jiechao Ma, Fujin He, Shu Zhang, Siyuan Pan, Yizhou Yu

{"title":"Advances in Deep Learning-Based Medical Image Analysis.","authors":"Xiaoqing Liu, Kunlun Gao, Bo Liu, Chengwei Pan, Kongming Liang, Lifeng Yan, Jiechao Ma, Fujin He, Shu Zhang, Siyuan Pan, Yizhou Yu","doi":"10.34133/2021/8786793","DOIUrl":"10.34133/2021/8786793","url":null,"abstract":"Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions.Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors.Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.","PeriodicalId":73207,"journal":{"name":"Health data science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47962962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implications of Delayed Reopening in Controlling the COVID-19 Surge in Southern and West-Central USA 延迟重新开放对控制美国南部和中西部新冠肺炎疫情激增的影响

Health data science Pub Date : 2020-12-03 DOI: 10.1101/2020.12.01.20242172

Raj Dandekar, Emma Wang, G. Barbastathis, Chris Rackauckas

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引用次数: 1

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