Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tackling the Problem of Large Deformations in Deep Learning Based
  Medical Image Registration Using Displacement Embeddings   Though, deep learning based medical image registration is currently starting
to show promising advances, often, it still fells behind conventional
frameworks in terms of registration accuracy. This is especially true for
applications where large deformations exist, such as registration of
interpatient abdominal MRI or inhale-to-exhale CT lung registration. Most
current works use U-Net-like architectures to predict dense displacement fields
from the input images in different supervised and unsupervised settings. We
believe that the U-Net architecture itself to some level limits the ability to
predict large deformations (even when using multilevel strategies) and
therefore propose a novel approach, where the input images are mapped into a
displacement space and final registrations are reconstructed from this
embedding. Experiments on inhale-to-exhale CT lung registration demonstrate the
ability of our architecture to predict large deformations in a single forward
path through our network (leading to errors below 2 mm).

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The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: math