  This paper presents our solution for the 2nd COVID-19 Severity Detection
Competition. This task aims to distinguish the Mild, Moderate, Severe, and
Critical grades in COVID-19 chest CT images. In our approach, we devise a novel
infection-aware 3D Contrastive Mixup Classification network for severity
grading. Specifcally, we train two segmentation networks to first extract the
lung region and then the inner lesion region. The lesion segmentation mask
serves as complementary information for the original CT slices. To relieve the
issue of imbalanced data distribution, we further improve the advanced
Contrastive Mixup Classification network by weighted cross-entropy loss. On the
COVID-19 severity detection leaderboard, our approach won the first place with
a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by
over 11.46%.
