Periphery-aware COVID-19 diagnosis with contrastive representation enhancement
| Autor: | Jilan Xu, Xiangyang Xue, Fei Shan, Yuejie Zhang, Junlin Hou, Longquan Jiang, Rui Feng, Du Shanshan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
Computer science 02 engineering and technology 01 natural sciences Article Periphery-aware Spatial Prediction (PSP) Community-acquired pneumonia Discriminative model Artificial Intelligence 0103 physical sciences Similarity (psychology) 0202 electrical engineering electronic engineering information engineering medicine Automated COVID-19 Diagnosis 010306 general physics business.industry Representation (systemics) Chest CT Images Pattern recognition medicine.disease Pneumonia Contrastive Representation Enhancement (CRE) Viral pneumonia Signal Processing 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Spatial prediction business Software |
| Zdroj: | Pattern Recognition |
| ISSN: | 0031-3203 |
| Popis: | 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 Repre-sentation 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. |
| Databáze: | OpenAIRE |
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