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Synthesizing medical images, such as PET, is a challenging task due to the fact that the intensity range is much wider and denser than those in photographs and digital renderings and are often heavily biased toward zero.
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Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)
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Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1125–1134 (2017)
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Choi, H., Lee, D.S.: Generation of structural mr images from amyloid pet: application to mr-less quantification. Journal of Nuclear Medicine 59
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Yan, Y., Lee, H., Somer, E., Grau, V.: Generation of amyloid pet images via conditional adversarial training for predicting progression to alzheimer’s disease. In: International Workshop on PRedictive Intelligence In MEdicine. pp. 26–33. Springer (2018)
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. North American Association for Computational Linguistics (NAACL) (2019)
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Lei, Y., Harms, J., Wang, T., Liu, Y., Shu, H.K., Jani, A.B., Curran, W.J., Mao, H., Liu, T., Yang, X.: Mri-only based synthetic ct generation using dense cycle consistent generative adversarial networks. Medical physics 46
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Ouyang, J., Chen, K.T., Gong, E., Pauly, J., Zaharchuk, G.: Ultra-low-dose pet reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Medical physics 46
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Rubin, J., Abulnaga, S.M.: Ct-to-mr conditional generative adversarial networks for ischemic stroke lesion segmentation. In: 2019 IEEE International Conference on Healthcare Informatics (ICHI). pp. 1–7. IEEE (2019)
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Yang, H., Sun, J., Carass, A., Zhao, C., Lee, J., Xu, Z., Prince, J.: Unpaired brain mr-to-ct synthesis using a structure-constrained cyclegan. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 174–182. Springer (2018)
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Chen, K.T., Gong, E., de Carvalho Macruz, F.B., Xu, J., Boumis, A., Khalighi, M., Poston, K.L., Sha, S.J., Greicius, M.D., Mormino, E., et al.: Ultra–low-dose 18f-florbetaben amyloid pet imaging using deep learning with multi-contrast mri inputs. Radiology 290
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