Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose
  CT Denoising   Low Dose CT Denoising research aims to reduce the risks of radiation exposure
to patients. Recently researchers have used deep learning to denoise low dose
CT images with promising results. However, approaches that use
mean-squared-error (MSE) tend to over smooth the image resulting in loss of
fine structural details in low contrast regions of the image. These regions are
often crucial for diagnosis and must be preserved in order for Low dose CT to
be used effectively in practice. In this work we use a cascade of two neural
networks, the first of which aims to reconstruct normal dose CT from low dose
CT by minimizing perceptual loss, and the second which predicts the difference
between the ground truth and prediction from the perceptual loss network. We
show that our method outperforms related works and more effectively
reconstructs fine structural details in low contrast regions of the image.

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The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin