Periphery-aware COVID-19 diagnosis with contrastive representation enhancement. (October 2021)

• Record Type: Journal Article
• Authors: Hou, Junlin; Xu, Jilan; Jiang, Longquan; Du, Shanshan; Feng, Rui; Zhang, Yuejie; Shan, Fei; Xue, Xiangyang
• Is Part Of: Pattern recognition. Volume 118(2021)
• Publication Date: 2021-10
• Subjects: Automated COVID-19 diagnosis -- Chest CT images -- Periphery-aware spatial prediction (PSP) -- Contrastive representation enhancement (CRE); Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108005
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel diagnosis approach with spatial pattern prior and representation enhancement mechanism is proposed to distinguish COVID-19 in the complex scenario of multi-type pneumonia classification. An important spatial pattern prior is introduced into the deep network by learning a Periphery-aware Spatial Prediction (PSP) task. An adaptive Contrastive Representation Enhancement (CRE) mechanism is designed to effectively capture the intra-class similarity and inter-class difference of various types of pneumonia. A large-scale chest CT dataset with 3D and 2D samples of four categories is collected for both volume-level and slice-level diagnosis research. Abstract: Computer-aided diagnosis has been extensively investigated for more rapid and accurate screening during the outbreak of COVID-19 epidemic. However, the challenge remains to distinguish COVID-19 in the complex scenario of multi-type pneumonia classification and improve the overall diagnostic performance. In this paper, we propose a novel periphery-aware COVID-19 diagnosis approach with contrastive representation enhancement to identify COVID-19 from influenza-A (H1N1) viral pneumonia, community acquired pneumonia (CAP), and healthy subjects using chest CT images. Our key contributions include: 1) an unsupervised Periphery-aware Spatial Prediction (PSP) task which is designed to introduce important spatial patterns into deep networks; 2) an adaptive Contrastive Representation Enhancement (CRE) mechanism which can effectively capture the intra-class similarity and inter-class difference of various types of pneumonia. We integrate PSP and CRE to obtain the representations which are highly discriminative in COVID-19 screening. We evaluate our approach comprehensively on our constructed large-scale dataset and two public datasets. Extensive experiments on both volume-level and slice-level CT images demonstrate the effectiveness of our proposed approach with PSP and CRE for COVID-19 diagnosis.