CMC-COV19D: Contrastive Mixup Classification for COVID-19 Diagnosis
Junlin Hou, Jilan Xu, Rui Feng, Yuejie Zhang, Fei Shan, Weiya Shi
Abstract
Deep learning methods have been extensively investigated for rapid and precise computer-aided diagnosis during the outbreak of the COVID-19 epidemic. However, there are still remaining issues to be addressed, such as distinguishing COVID-19 in the complex scenario of multi-type pneumonia classification. In this paper, we aim to boost the COVID-19 diagnostic performance with more discriminative deep representations of COVID and non-COVID categories. We propose a novel COVID-19 diagnosis approach with contrastive representation learning to effectively capture the intra-class similarity and inter-class difference. Besides, we design an adaptive joint training strategy to integrate the classification loss, mixup loss, and contrastive loss. Through the joint loss function, we obtain the high-level representations which are highly discriminative in COVID-19 screening. Extensive experiments on two chest CT image datasets, i.e., CC-CCII dataset and COV19-CT-DB database, demonstrate the effectiveness of our proposed approach in COVID-19 diagnosis. Our method won the first prize in the ICCV 2021 Covid-19 Diagnosis Competition of AI-enabled Medical Image Analysis Workshop. Our code is publicly available at https://github.com/houjunlin/Team-FDVTS-COVID-Solution.