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

Heng Li, Yan Hu*, Sanqian Li, Wenjun Lin, Peng Liu, Risa Higashita, Jiang Liu

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

12 引用 (Scopus)

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.

源语言英语
主期刊名Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings
编辑De-Shuang Huang, Kang-Hyun Jo
出版商Springer Science and Business Media Deutschland GmbH
413-422
页数10
ISBN(印刷版)9783030608019
DOI
出版状态已出版 - 2020
已对外发布
活动16th International Conference on Intelligent Computing, ICIC 2020 - Bari , 意大利
期限: 2 10月 20205 10月 2020

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12464 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议16th International Conference on Intelligent Computing, ICIC 2020
国家/地区意大利
Bari
时期2/10/205/10/20

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引用此

Li, H., Hu, Y., Li, S., Lin, W., Liu, P., Higashita, R., & Liu, J. (2020). CT Scan Synthesis for Promoting Computer-Aided Diagnosis Capacity of COVID-19. 在 D.-S. Huang, & K.-H. Jo (编辑), Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings (页码 413-422). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 12464 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60802-6_36