Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised learning for cross-domain medical image synthesis using
  deformation invariant cycle consistency networks   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. This paper presents a
deformation-invariant CycleGAN (DicycleGAN) method using deformable
convolutional layers and new cycle-consistency losses. Its robustness dealing
with data that suffer from domain-specific nonlinear deformations has been
evaluated through comparison experiments performed on a multi-sequence brain MR
dataset and a multi-modality abdominal dataset. Our method has displayed its
ability to generate synthesized data that is aligned with the source while
maintaining a proper quality of signal compared to CycleGAN-generated data. The
proposed model also obtained comparable performance with CycleGAN when data
from the source and target domains are alignable through simple affine
transformations.

21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys