Abstract
A novel WGAN-GP-based model is proposed in this study to fulfill bi-directional synthesis of medical images for the first time. GMM-based noise generated from the Glow model is newly incorporated into the WGAN-GP-based model to better reflect the characteristics of heterogeneity commonly seen in medical images, which is beneficial to produce high-quality synthesized medical images. Both the conventional “down-sampling”-like synthesis and the more challenging “up-sampling”-like synthesis are realized through the newly introduced model, which is thoroughly evaluated with comparisons towards several popular deep learning-based models both qualitatively and quantitatively. The superiority of the new model is substantiated based on a series of rigorous experiments using a multi-modal MRI database composed of 355 real demented patients in this study, from the statistical perspective.
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Acknowledgements
This work was jointly supported by the grant 61862043 approved by National Natural Science Foundation of China, and the key grant 20181ACB20006 approved by Natural Science Foundation of Jiangxi Province.
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Huang, W., Luo, M., Liu, X., Zhang, P., Ding, H., Ni, D. (2019). Novel Bi-directional Images Synthesis Based on WGAN-GP with GMM-Based Noise Generation. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_19
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DOI: https://doi.org/10.1007/978-3-030-32692-0_19
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