Trans-cGAN: transformer-Unet-based generative adversarial networks for cross-modality magnetic resonance image synthesis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s11222-023-10282-8.pdf
Reference46 articles.
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3. Armanious, K., Jiang, C., Fischer, M., et al.: MedGAN: medical image translation using GANs. Comput. Med. Imaging Graphics 79(101), 684 (2020). https://doi.org/10.1016/j.compmedimag.2019.101684
4. Benzakoun, J., Deslys, M.A., Legrand, L., et al.: Synthetic flair as a substitute for flair sequence in acute ischemic stroke. Radiology 303(1), 153–159 (2022)
5. Bi, L., Kim, J., Kumar, A., et al.: Synthesis of positron emission tomography (PET) images via multi-channel generative adversarial networks (gans). In: Cardoso, M.J., Arbel, T., Gao, F., et al. (eds.) Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment—Fifth International Workshop, CMMI 2017, Second International Workshop,RAMBO 2017, and First International Workshop, SWITCH 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings, Lecture Notes in Computer Science, vol. 10555, pp 43–51. Springer (2017). https://doi.org/10.1007/978-3-319-67564-0_5
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