Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks

12 Aug 2018 • Chengjia Wang • Gillian Macnaught • Giorgos Papanastasiou • Tom MacGillivray • David Newby

Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear deformations captured by CycleGAN make the synthesized images difficult to be used for some applications, for example, generating pseudo-CT for PET-MR attenuation correction... (read more)

PDF Abstract

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
Batch Normalization	Normalization
Residual Connection	Skip Connections
PatchGAN	Discriminators
ReLU	Activation Functions
Tanh Activation	Activation Functions
Residual Block	Skip Connection Blocks
Instance Normalization	Normalization
Convolution	Convolutions
Leaky ReLU	Activation Functions
Sigmoid Activation	Activation Functions
GAN Least Squares Loss	Loss Functions
Cycle Consistency Loss	Loss Functions
CycleGAN	Generative Models

METHOD

TYPE

Batch Normalization