作者
Bo Wang, Shuo Jin, Qingsen Yan, Haibo Xu, Chuan Luo, Lai Wei, Wei Zhao, Xuexue Hou, Wenshuo Ma, Zhengqing Xu, Zhuozhao Zheng, Wenbo Sun, Lan Lan, Wei Zhang, Xiangdong Mu, Chenxi Shi, Zhongxiao Wang, Jihae Lee, Zijian Jin, Minggui Lin, Hongbo Jin, Liang Zhang, Jun Guo, Benqi Zhao, Zhizhong Ren, Shuhao Wang, Wei Xu, Xinghuan Wang, Jianming Wang, Zheng You, Jiahong Dong
发表日期
2021/1/1
期刊
Applied soft computing
卷号
98
页码范围
106897
出版商
Elsevier
简介
The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%–40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a …
引用总数
20202021202220232024356786025
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