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.
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%0 Journal Article
%1 journals/corr/abs-2311-05697
%A Shi, Yu
%A Tang, Hannah
%A Baine, Michael
%A Hollingsworth, Michael A.
%A Du, Huijing
%A Zheng, Dandan
%A Zhang, Chi
%A Yu, Hongfeng
%D 2023
%J CoRR
%K dblp
%T 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.
%U http://dblp.uni-trier.de/db/journals/corr/corr2311.html#abs-2311-05697
%V abs/2311.05697
@article{journals/corr/abs-2311-05697,
added-at = {2024-08-01T00:00:00.000+0200},
author = {Shi, Yu and Tang, Hannah and Baine, Michael and Hollingsworth, Michael A. and Du, Huijing and Zheng, Dandan and Zhang, Chi and Yu, Hongfeng},
biburl = {https://www.bibsonomy.org/bibtex/2af09b396607994689aee4259025affc1/dblp},
ee = {https://doi.org/10.48550/arXiv.2311.05697},
interhash = {24298716daa3f9d819cb43dad7a6595f},
intrahash = {af09b396607994689aee4259025affc1},
journal = {CoRR},
keywords = {dblp},
timestamp = {2024-08-05T07:08:45.000+0200},
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.},
url = {http://dblp.uni-trier.de/db/journals/corr/corr2311.html#abs-2311-05697},
volume = {abs/2311.05697},
year = 2023
}
