Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fully-Automated Analysis of Body Composition from CT in Cancer Patients
  Using Convolutional Neural Networks   The amounts of muscle and fat in a person's body, known as body composition,
are correlated with cancer risks, cancer survival, and cardiovascular risk. The
current gold standard for measuring body composition requires time-consuming
manual segmentation of CT images by an expert reader. In this work, we describe
a two-step process to fully automate the analysis of CT body composition using
a DenseNet to select the CT slice and U-Net to perform segmentation. We train
and test our methods on independent cohorts. Our results show Dice scores
(0.95-0.98) and correlation coefficients (R=0.99) that are favorable compared
to human readers. These results suggest that fully automated body composition
analysis is feasible, which could enable both clinical use and large-scale
population studies.

31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: econ
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin