Image synthesis from corrupted contrasts increases the diver-
sity of diagnostic information available for many neurological diseases.
Recently the image-to-image translation has experienced signi cant lev-
els of interest within medical research, beginning with the successful use
of the Generative Adversarial Network (GAN) to the introduction of
cyclic constraint extended to multiple domains. However, in current ap-
proaches, there is no guarantee that the mapping between the two image
domains would be unique or one-to-one. In this paper, we introduce a
novel approach to unpaired image-to-image translation based on
the invertible architecture. The invertible property of the
ow-based
architecture assures a cycle-consistency of image-to-image translation
without additional loss functions. We utilize the temporal informa-
tion between consecutive slices to provide more constraints to
the optimization for transforming one domain to another in un-
paired volumetric medical images. To capture temporal structures
in the medical images, we explore the displacement between the consec-
utive slices using a deformation eld. In our approach, the deformation
eld is used as a guidance to keep the translated slides realistic and con-
sistent across the translation. The experimental results have shown that
the synthesized images using our proposed approach are able to archive
a competitive performance in terms of mean squared error, peak signal-
to-noise ratio, and structural similarity index when compared with the
existing deep learning-based methods on three standard datasets, i.e.
HCP, MRBrainS13 and Brats2019.
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Learning cycle-consistent cooperative networks via alternating MCMC teaching for unsupervised cross-domain translation
This paper studies the unsupervised cross-domain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a generative cooperative network that consists of an energy based model and a latent variable model. The use of generative cooperative network enables maximum likelihood learning of the domain model by MCMC teaching, where the energy-based model seeks to fit the data distribution of domain
and distills its knowledge to the latent variable model via MCMC. Specifically, in the MCMC teaching process, the latent variable model parameterized by an encoder-decoder maps examples from the source domain to the target domain, while the energy-based model further refines the mapped results by Langevin revision such that the revised results match to the examples in the target domain in terms of the statistical properties, which are defined by the learned energy function. For the purpose of building up a correspondence between two unpaired domains, the proposed framework simultaneously learns a pair of cooperative networks with cycle consistency, accounting for a two-way translation between two domains, by alternating MCMC teaching. Experiments show that the proposed framework is useful for unsupervised image-to-image translation and unpaired image sequence translation.
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- Award ID(s):
- 2015577
- NSF-PAR ID:
- 10289685
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- ISSN:
- 2159-5399
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
