ArXiv Preprint
Since 2019, coronavirus Disease 2019 (COVID-19) has been widely spread and
posed a serious threat to public health. Chest Computed Tomography (CT) holds
great potential for screening and diagnosis of this disease. The segmentation
of COVID-19 CT imaging can achieves quantitative evaluation of infections and
tracks disease progression. COVID-19 infections are characterized by high
heterogeneity and unclear boundaries, so capturing low-level features such as
texture and intensity is critical for segmentation. However, segmentation
networks that emphasize low-level features are still lacking. In this work, we
propose a DECOR-Net capable of capturing more decorrelated low-level features.
The channel re-weighting strategy is applied to obtain plenty of low-level
features and the dependencies between channels are reduced by proposed
decorrelation loss. Experiments show that DECOR-Net outperforms other
cutting-edge methods and surpasses the baseline by 5.1% and 4.9% in terms of
Dice coefficient and intersection over union. Moreover, the proposed
decorrelation loss can improve the performance constantly under different
settings. The Code is available at https://github.com/jiesihu/DECOR-Net.git.
Jiesi Hu, Yanwu Yang, Xutao Guo, Ting Ma
2023-02-28
