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.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11861 LNCS, pp. 160–168). Springer. https://doi.org/10.1007/978-3-030-32692-0_19
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