Kidney Tumor Segmentation Using a Hybrid CNN-Transformer Network for Partial Nephrectomy Planning
Author:
Funder
Ministry of Health and Welfare
Seoul Women`s University
Publisher
Korea Computer Graphics Society
Link
http://journal.cg-korea.org/download/download_pdf?doi=10.15701/kcgs.2024.30.4.11
Reference19 articles.
1. M.M. Picken, W. Lu, and N.G. Gopal, “Positive Surgical Margins in Renal Cell Carcinoma: Translating Tumor Biology Into Clinical Outcomes,” Amercian Journal of Clinical Pathology, 143(5), pp. 620-622, 2015. 10.1309/AJCP9KVHJRXF6DBZ 25873493
2. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Medical Image Computing and Computer Assisted Intervention (MICCAI) 2015, pp. 234-241, 2015. 10.1007/978-3-319-24574-4_28
3. P. Sun, Z. Mo, F. Hu, F. Liu, T. Mo, Y. Zhang, et al., “Kidney Tumor Segmentation Based on FR2PAttU-Net Model,” Frontiers in Oncology, 12, pp. 1-13, 2022. 10.3389/fonc.2022.853281 35372025 PMC8968695
4. J. Guo, W. Zeng, S. Yu, and J. Xiao, “RAU-Net: U-Net Model Based on Residual and Attention for Kidney and Kidney Tumor Segmentation,” IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 353-356, 2021. 10.1109/ICCECE51280.2021.9342530
5. A. Myronenko, and A. Hatamizadeh, “3D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks,” arXiv:1909.06684, 2019. 10.24926/548719.009
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