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
- DOI
- 10.3390/cancers15235496
- Published
- 2023-11-21
- Container
- Cancers
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/cancers15235496,
title = {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},
author = {Yu Shi and Hannah Tang and Michael J. Baine and Michael A. Hollingsworth and Huijing Du and Dandan Zheng and Chi Zhang and Hongfeng Yu},
year = {2023},
journal = {Cancers},
doi = {10.3390/cancers15235496},
url = {https://doi.org/10.3390/cancers15235496}
}RIS
TY - JOUR TI - 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 AU - Yu Shi AU - Hannah Tang AU - Michael J. Baine AU - Michael A. Hollingsworth AU - Huijing Du AU - Dandan Zheng AU - Chi Zhang AU - Hongfeng Yu PY - 2023 JO - Cancers DO - 10.3390/cancers15235496 UR - https://doi.org/10.3390/cancers15235496 ER -
APA
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. https://doi.org/10.3390/cancers15235496
Source records
- crossref · retrieved 2026-11-10T09:39:36.187Z