SDGAN: A novel spatial deformable generative adversarial network for low-dose CT image reconstruction - researchr publication related

researchr

You are not signed in
Sign in
Sign up

Ying Liu, Guangyu Wu, Zhongwei Lv. SDGAN: A novel spatial deformable generative adversarial network for low-dose CT image reconstruction. Displays, 78:102405, July 2023. [doi]

The following publications are possibly variants of this publication:

Learning to Fuse Music Genres with Generative Adversarial Dual LearningZhiqian Chen, Chih-Wei Wu, Yen-Cheng Lu, Alexander Lerch, Chang-Tien Lu. In Proceedings of the International Conference on Data Mining (ICDM). 2017:

Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer LearningMing Li, Jiping Wang, Yang Chen 0008, Yufei Tang, Zhongyi Wu, Yujin Qi, Haochuan Jiang, Jian Zheng 0001, Benjamin M. W. Tsui. tmi, 42(9):2616-2630, September 2023. [doi]

A novel super-resolution CT image reconstruction via semi-supervised generative adversarial networkXin Jiang, Mingzhe Liu, Feixiang Zhao, Xianghe Liu, Helen Zhou. nca, 32(18):14563-14578, 2020. [doi]

Adaptive Non-Local Generative Adversarial Networks for Low-Dose CT Image DenoisingLinlin Yang, Hongying Liu, Fanhua Shang, Yuanyuan Liu 0001. icassp 2023: 1-5 [doi]

runs on WebDSL