Shi, Y.; Tang, H.; Baine, M.J.; Hollingsworth, M.A.; Du, H.; Zheng, D.; Zhang, C.; Yu, H.
3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer. Cancers 2023, 15, 5496.
https://doi.org/10.3390/cancers15235496
AMA Style
Shi Y, Tang H, Baine MJ, Hollingsworth MA, Du H, Zheng D, Zhang C, Yu H.
3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer. Cancers. 2023; 15(23):5496.
https://doi.org/10.3390/cancers15235496
Chicago/Turabian Style
Shi, Yu, Hannah Tang, Michael J. Baine, Michael A. Hollingsworth, Huijing Du, Dandan Zheng, Chi Zhang, and Hongfeng Yu.
2023. "3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer" Cancers 15, no. 23: 5496.
https://doi.org/10.3390/cancers15235496
APA Style
Shi, Y., Tang, H., Baine, M. J., Hollingsworth, M. A., Du, H., Zheng, D., Zhang, C., & Yu, H.
(2023). 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer. Cancers, 15(23), 5496.
https://doi.org/10.3390/cancers15235496
