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Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks
Chengjia Wang
*
, Gillian Macnaught
, Giorgos Papanastasiou
, Tom MacGillivray
, David Newby
*
Corresponding author for this work
Computer Science
School of Mathematical & Computer Sciences
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
28
Citations (Scopus)
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INIS
data
100%
images
100%
synthesis
100%
learning
100%
deformation
100%
comparative evaluations
50%
nonlinear problems
50%
datasets
50%
applications
25%
losses
25%
layers
25%
performance
25%
transformations
25%
signals
25%
computerized tomography
25%
corrections
25%
attenuation
25%
brain
25%
positron computed tomography
25%
Engineering
Image Synthesis
100%
Medical Image
100%
Deformation Invariant
100%
Nonlinear Deformation
100%
Convolutional Layer
50%
Affine Transformation
50%
Computer Science
Unsupervised Learning
100%
Generative Adversarial Networks
100%
Image Synthesis
100%
Medical Domain
100%
Multimodality
20%
Affine Transformation
20%
Convolutional Layer
20%
Chemical Engineering
Unsupervised Learning
100%
Polyethylene Terephthalate
100%