Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unimodal Cyclic Regularization for Training Multimodal Image
  Registration Networks   The loss function of an unsupervised multimodal image registration framework
has two terms, i.e., a metric for similarity measure and regularization. In the
deep learning era, researchers proposed many approaches to automatically learn
the similarity metric, which has been shown effective in improving registration
performance. However, for the regularization term, most existing multimodal
registration approaches still use a hand-crafted formula to impose artificial
properties on the estimated deformation field. In this work, we propose a
unimodal cyclic regularization training pipeline, which learns task-specific
prior knowledge from simpler unimodal registration, to constrain the
deformation field of multimodal registration. In the experiment of abdominal
CT-MR registration, the proposed method yields better results over conventional
regularization methods, especially for severely deformed local regions.

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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: patt-sol