ref [102] · 2509.06365 · notice #8432 · dispute
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"Denoising simulated low -field MRI (70mT) using denoising autoencoders (DAE) and cycle - consistent generative adversarial networks (cycle -GAN)". arXiv preprint arXiv:2307.06338. 2023 Jul 12. [102] Ali, H., Biswas, M.R., Mohsen, F. et al. The role of generative adversarial networks in brain MRI: a scoping review. Insights Imaging 13, 98 (2022). https://doi.org/10.1186/s13244-022-01237-0 [103] Makhlouf, A., Maayah, M., Abughanam, N. et al. The use of generative adversarial networks in medical image augmentation. Neural Comput & Applic 35, 24055 -24068 (2023). https://doi.org/10.1007/s00521-023-09100-z [104] Ayde R, Vornehm M, Zhao Y, Knoll F, Wu EX, Sarracanie M. MRI at low field: A review of software solutions for improving SNR.
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"Denoising simulated low -field MRI (70mT) using denoising autoencoders (DAE) and cycle - consistent generative adversarial networks (cycle -GAN)". arXiv preprint arXiv:2307.06338. 2023 Jul 12. [102] Ali, H., Biswas, M.R., Mohsen, F. et al. The role of generative adversarial networks in brain MRI: a scoping review. Insights Imaging 13, 98 (2022). https://doi.org/10.1186/s13244-022-01237-0 [103] Makhlouf, A., Maayah, M., Abughanam, N. et al. The use of generative adversarial networks in medical image augmentation. Neural Comput & Applic 35, 24055 -24068 (2023). https://doi.org/10.1007/s00521-023-09100-z [104] Ayde R, Vornehm M, Zhao Y, Knoll F, Wu EX, Sarracanie M. MRI at low field: A review of software solutions for improving SNR