Novel Bi-directional Images Synthesis Based on WGAN-GP with GMM-Based Noise Generation

摘要

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 Mill database composed of 355 real demented patients in this study, from the statistical perspective.