Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning to estimate the physical proportion of infected region of
  lung for COVID-19 pneumonia with CT image set   Utilizing computed tomography (CT) images to quickly estimate the severity of
cases with COVID-19 is one of the most straightforward and efficacious methods.
Two tasks were studied in this present paper. One was to segment the mask of
intact lung in case of pneumonia. Another was to generate the masks of regions
infected by COVID-19. The masks of these two parts of images then were
converted to corresponding volumes to calculate the physical proportion of
infected region of lung. A total of 129 CT image set were herein collected and
studied. The intrinsic Hounsfiled value of CT images was firstly utilized to
generate the initial dirty version of labeled masks both for intact lung and
infected regions. Then, the samples were carefully adjusted and improved by two
professional radiologists to generate the final training set and test
benchmark. Two deep learning models were evaluated: UNet and 2.5D UNet. For the
segment of infected regions, a deep learning based classifier was followed to
remove unrelated blur-edged regions that were wrongly segmented out such as air
tube and blood vessel tissue etc. For the segmented masks of intact lung and
infected regions, the best method could achieve 0.972 and 0.757 measure in mean
Dice similarity coefficient on our test benchmark. As the overall proportion of
infected region of lung, the final result showed 0.961 (Pearson's correlation
coefficient) and 11.7% (mean absolute percent error). The instant proportion of
infected regions of lung could be used as a visual evidence to assist clinical
physician to determine the severity of the case. Furthermore, a quantified
report of infected regions can help predict the prognosis for COVID-19 cases
which were scanned periodically within the treatment cycle.

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The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an