Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Convolutional Neural Networks for Imaging Data Based Survival
  Analysis of Rectal Cancer   Recent radiomic studies have witnessed promising performance of deep learning
techniques in learning radiomic features and fusing multimodal imaging data.
Most existing deep learning based radiomic studies build predictive models in a
setting of pattern classification, not appropriate for survival analysis
studies where some data samples have incomplete observations. To improve
existing survival analysis techniques whose performance is hinged on imaging
features, we propose a deep learning method to build survival regression models
by optimizing imaging features with deep convolutional neural networks (CNNs)
in a proportional hazards model. To make the CNNs applicable to tumors with
varied sizes, a spatial pyramid pooling strategy is adopted. Our method has
been validated based on a simulated imaging dataset and a FDG-PET/CT dataset of
rectal cancer patients treated for locally advanced rectal cancer. Compared
with survival prediction models built upon hand-crafted radiomic features using
Cox proportional hazards model and random survival forests, our method achieved
competitive prediction performance.

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The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph