TY - GEN
T1 - Novel Bi-directional Images Synthesis Based on WGAN-GP with GMM-Based Noise Generation
AU - Huang, Wei
AU - Luo, Mingyuan
AU - Liu, Xi
AU - Zhang, Peng
AU - Ding, Huijun
AU - Ni, Dong
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Dementia diseases diagnosis
KW - Generative adversarial network
KW - Medical images synthesis
UR - http://www.scopus.com/inward/record.url?scp=85075672345&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32692-0_19
DO - 10.1007/978-3-030-32692-0_19
M3 - 会议稿件
AN - SCOPUS:85075672345
SN - 9783030326913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 160
EP - 168
BT - Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Suk, Heung-Il
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
PB - Springer
T2 - 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -