Open dyollb opened 3 years ago
thanks for the request, as a reference, are you aware of any open-source implementation in any programming language?
For the original un-paired cyclegan
(and paired pix2pix
): https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
pix2pix
works nicely if the images are provided as registered pairs. But quality deteriorates if registration is not good.
I did not find any implementation of a cyclegan using the mutual information loss, or other losses (e.g. gradient consistency loss) developed to preserve the geometry/boundaries.
The paper by Modanwal et al uses Mutual-Information-Neural-Estimation (MINE) to compute mutual information (paper, code)
Is your feature request related to a problem? Please describe. Provide an style transfer model to learn to generate synthetic CT from MRI, or T2 from T1, or e.g. T1 from on scanner to T1 from a different scanner/site without loosing anatomical/geometric information.
Describe the solution you'd like Add a CycleGAN with mutual information as a consistency loss, e.g. implementing https://arxiv.org/abs/1912.08061
Modanwal, Gourav, Adithya Vellal, and Maciej A. Mazurowski. "Normalization of breast MRIs using Cycle-Consistent Generative Adversarial Networks." Computer Methods and Programs in Biomedicine (2021): 106225.
Describe alternatives you've considered It would be nice to have a tutorial illustrating different losses.
Additional context