Spectrum and Style Transformation Framework for Omni-Domain COVID-19 Diagnosis

Wang, Zhenkun1; Gui, Shuangchun1; Ding, Xinpeng2; Hu, Xiaowei3; Xu, Xiaowei4; Li, Xiaomeng2

2022-11-01

10.1109/TETCI.2022.3219858

IEEE Transactions on Emerging Topics in Computational Intelligence   Impact Factor & Quartile

2471-285X

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic and profoundly affects almost all people around the world. Thus, many automatic diagnosis methods based on computed tomography (CT) images have been proposed to reduce the workload of radiologists. Most of the existing methods focus on the in-domain predictions, i.e., the training and testing have similar distributions, which is impractical in real-world situations, since the CT images can be collected from different devices and in different hospitals. To improve the diagnosis performance of COVID-19 for both in-domain and out-of-domain data, this paper proposes a spectrum and style transformation framework for omni-domain COVID-19 diagnosis. To achieve this, we first present a spectrum transform module, which helps to discover the discriminating features of each domain to recognize the in-domain data. Then, we formulate a cross-domain stylization module, which learns the cross-domain knowledge to enhance the model generalization capability to deal with out-of-domain data. Moreover, our framework is a plug-and-play module that can be easily integrated into existing deep models. We evaluate our framework on four COVID-19 datasets and show our method consistently improves the diagnosis performance of various methods on both in-domain and out-of-domain data.

COVID-19 diagnosis image classification data augmentation and cross-domain learning

SCI

English

First

Computer Science

Computer Science, Artificial Intelligence

WOS:000886903000001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Web of Science

1.Southern Univ Sci & Technol, Sch Syst Design & Intelligent Mfg SDIM, Shenzhen 518055, Peoples R China
2.Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Hong Kong, Peoples R China
3.Shanghai AI Lab, Shanghai 200232, Peoples R China
4.Guangdong Acad Med Sci, Guangdong Cardiovasc Inst, Guang dong Prov Peoples Hosp, Guangdong Prov Key Lab SouthChina Struct Heart Dis, Guangzhou 510050, Peoples R China

Wang, Zhenkun,Gui, Shuangchun,Ding, Xinpeng,et al. Spectrum and Style Transformation Framework for Omni-Domain COVID-19 Diagnosis[J]. IEEE Transactions on Emerging Topics in Computational Intelligence,2022,PP(99):1-12.

Wang, Zhenkun,Gui, Shuangchun,Ding, Xinpeng,Hu, Xiaowei,Xu, Xiaowei,&Li, Xiaomeng.(2022).Spectrum and Style Transformation Framework for Omni-Domain COVID-19 Diagnosis.IEEE Transactions on Emerging Topics in Computational Intelligence,PP(99),1-12.

Wang, Zhenkun,et al."Spectrum and Style Transformation Framework for Omni-Domain COVID-19 Diagnosis".IEEE Transactions on Emerging Topics in Computational Intelligence PP.99(2022):1-12.