Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Adversarial Registration for Improved Conditional Deformable
  Templates   Deformable templates are essential to large-scale medical image registration,
segmentation, and population analysis. Current conventional and deep
network-based methods for template construction use only regularized
registration objectives and often yield templates with blurry and/or
anatomically implausible appearance, confounding downstream biomedical
interpretation. We reformulate deformable registration and conditional template
estimation as an adversarial game wherein we encourage realism in the moved
templates with a generative adversarial registration framework conditioned on
flexible image covariates. The resulting templates exhibit significant gain in
specificity to attributes such as age and disease, better fit underlying
group-wise spatiotemporal trends, and achieve improved sharpness and
centrality. These improvements enable more accurate population modeling with
diverse covariates for standardized downstream analyses and easier anatomical
delineation for structures of interest.

4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas