The following publications are possibly variants of this publication:
- Liver Tumor Segmentation Based on Multi-Scale and Self-Attention MechanismFuFang Li, Manlin Luo, Ming Hu, Guobin Wang, Yan Chen. csse, 47(3):2835-2850, 2023. [doi]
- Attention-Guided Deep Neural Network With Multi-Scale Feature Fusion for Liver Vessel SegmentationQingsen Yan, Bo Wang, Wei Zhang, Chuan Luo, Wei Xu, Zhengqing Xu, Yanning Zhang, Qinfeng Shi, Liang Zhang, Zheng You. titb, 25(7):2629-2642, 2021. [doi]
- SAA-Net: U-shaped network with Scale-Axis-Attention for liver tumor segmentationChi Zhang, Jingben Lu, Qianqian Hua, Chunguo Li, Pengwei Wang. bspc, 73:103460, 2022. [doi]
- CPAD-Net: Contextual parallel attention and dilated network for liver tumor segmentationXuehu Wang, Shuping Wang, Zhiling Zhang, Xiaoping Yin, Tianqi Wang, Nie Li. bspc, 79(Part):104258, 2023. [doi]
- A Densely Connected UNet3D Network Combined Attention Mechanism for Liver and Tumor SegmentationHewen Xi, Junxi Chen, Dongping Xiong, Xiaofeng He, Aiping Qu, Lingna Chen. jmihi, 11(5):1463-1470, 2021. [doi]
- Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-NetYun Wu, Huaiyan Shen, Yaya Tan, Yucheng Shi. cars, 17(10):1915-1922, 2022. [doi]