The following publications are possibly variants of this publication:
- CGS-Net: A classification-guided framework for automated infection segmentation of COVID-19 from CT imagesWen Zhou, Jihong Wang, Yuhang Wang, Zijie Liu, Chen Yang 0016. imst, 34(1), January 2024. [doi]
- CdcSegNet: Automatic COVID-19 Infection Segmentation From CT ImagesJu Zhang 0004, Dechen Chen, Dong Ma, Ying-Chang Liang, Xiaoyan Sun, Xiaobing Xu, Yun Cheng. tim, 72:1-13, 2023. [doi]
- Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT ImagesDeng-Ping Fan, Tao Zhou 0002, Ge-Peng Ji, Yi Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, Ling Shao 0001. tmi, 39(8):2626-2637, 2020. [doi]
- A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT imagesHan Chen, Yifan Jiang 0002, Hanseok Ko, Murray H. Loew. bspc, 79(Part):104250, 2023. [doi]
- P2P-COVID-GAN: Classification and Segmentation of COVID-19 Lung Infections From CT Images Using GANR. Nandhini Abirami, Durai Raj Vincent P. M., Seifedine Kadry 0001. jdwm, 17(4):101-118, 2021. [doi]
- MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT imagesJianning Chi, Shuang Zhang, Xiaoying Han, Huan Wang, Chengdong Wu, Xiaosheng Yu. spic, 108:116835, 2022. [doi]