中文版 | English
题名

CT Scan Synthesis for Promoting Computer-Aided Diagnosis Capacity of COVID-19

作者
通讯作者Hu,Yan
DOI
发表日期
2020
会议名称
International Conference on Intelligent Computing
ISSN
0302-9743
EISSN
1611-3349
会议录名称
卷号
12464 LNCS
页码
413-422
会议日期
October 2-5
会议地点
Bari, Italy
摘要

Nowadays, with the rapid spread of Corona Virus Disease 2019 (COVID-19), this epidemic has become a threatening risk for global public health. Medical workers and researchers all over the world are struggling against the novel coronavirus in the front line. Because the computed tomography (CT) images from infected patients exposure characteristic abnormalities, automatic CT analyzers based on AI-based algorithms are extensively employed as effective weapons to aid clinicians. However, unbalanced data and lack of annotations obstruct AI-based algorithms applying in aided diagnosis because of their low performance. Therefore, in order to solve the above problems, a general-purpose solution is proposed to synthesize COVID-19 CT scans from non-COVID-19 data for providing high-quality negative-positive paired CT scans. Particularly, we introduce an elastic registration algorithm of CT images to manufacture paired training data. Then, a conditional Generative Adversarial Networks (GANs) based image-to-image translation model is implemented to synthesize COVID-19 CT scans from non-COVID-19 data. The effectiveness of our proposed algorithm used in COVID-19 aided diagnosis is verified in the experiments, and the identification and detection capacities of the classification models have been enhanced with the generated CT scans. Specifically, the precise lesion location is achieved by the generated data with a weakly supervised algorithm of class activation mapping (CAM). The model and code of this paper are publicly available at https://github.com/lihengbit/Synthesis-of-COVID-19-CT-Scan.

关键词
学校署名
第一 ; 通讯
语种
英语
相关链接[Scopus记录]
收录类别
EI入藏号
20204409420222
EI主题词
Generative adversarial networks ; Computerized tomography ; Health risks
EI分类号
Biomedical Engineering:461.1 ; Health Care:461.7 ; Artificial Intelligence:723.4 ; Computer Applications:723.5
Scopus记录号
2-s2.0-85094169883
来源库
Scopus
引用统计
被引频次[WOS]:0
成果类型会议论文
条目标识符http://kc.sustech.edu.cn/handle/2SGJ60CL/209277
专题工学院_计算机科学与工程系
作者单位
1.School of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Big Data Research Center,University of Electronic Science and Technology of China,Chengdu,611731,China
3.Tomey Corporation,Nagoya,451-0051,Japan
4.Department of Computer Science and Engineering,Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,518055,China
5.Ningbo Institute of Industrial Technology Chinese Academy of Sciences,Ningbo,China
第一作者单位南方科技大学
通讯作者单位南方科技大学
第一作者的第一单位南方科技大学
推荐引用方式
GB/T 7714
Li,Heng,Hu,Yan,Li,Sanqian,et al. CT Scan Synthesis for Promoting Computer-Aided Diagnosis Capacity of COVID-19[C],2020:413-422.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可 操作
Li2020_Chapter_CTSca(3793KB)----限制开放--
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