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%.

本文提出了一种新型的基于感染的 3D 对比混合分类网络用于 COVID-19 胸部 CT 图像的病情评估，通过使用分割网络提取肺部和内部病变区域并使用加权交叉熵损失函数来改进模型性能，分数显著优于基线模型。