Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CT Image Denoising with Perceptive Deep Neural Networks   Increasing use of CT in modern medical practice has raised concerns over
associated radiation dose. Reduction of radiation dose associated with CT can
increase noise and artifacts, which can adversely affect diagnostic confidence.
Denoising of low-dose CT images on the other hand can help improve diagnostic
confidence, which however is a challenging problem due to its ill-posed nature,
since one noisy image patch may correspond to many different output patches. In
the past decade, machine learning based approaches have made quite impressive
progress in this direction. However, most of those methods, including the
recently popularized deep learning techniques, aim for minimizing
mean-squared-error (MSE) between a denoised CT image and the ground truth,
which results in losing important structural details due to over-smoothing,
although the PSNR based performance measure looks great. In this work, we
introduce a new perceptual similarity measure as the objective function for a
deep convolutional neural network to facilitate CT image denoising. Instead of
directly computing MSE for pixel-to-pixel intensity loss, we compare the
perceptual features of a denoised output against those of the ground truth in a
feature space. Therefore, our proposed method is capable of not only reducing
the image noise levels, but also keeping the critical structural information at
the same time. Promising results have been obtained in our experiments with a
large number of CT images.

12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an