Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Normalized Fully Convolutional Approach to Head and Neck Cancer
  Outcome Prediction   In medical imaging, radiological scans of different modalities serve to
enhance different sets of features for clinical diagnosis and treatment
planning. This variety enriches the source information that could be used for
outcome prediction. Deep learning methods are particularly well-suited for
feature extraction from high-dimensional inputs such as images. In this work,
we apply a CNN classification network augmented with a FCN preprocessor
sub-network to a public TCIA head and neck cancer dataset. The training goal is
survival prediction of radiotherapy cases based on pre-treatment FDG PET-CT
scans, acquired across 4 different hospitals. We show that the preprocessor
sub-network in conjunction with aggregated residual connection leads to
improvements over state-of-the-art results when combining both CT and PET input
images.

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The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an