Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Brain Tumour Segmentation and Biophysics-Guided Survival
  Prediction   Gliomas are the most common malignant brain tumourswith intrinsic
heterogeneity. Accurate segmentation of gliomas and theirsub-regions on
multi-parametric magnetic resonance images (mpMRI)is of great clinical
importance, which defines tumour size, shape andappearance and provides
abundant information for preoperative diag-nosis, treatment planning and
survival prediction. Recent developmentson deep learning have significantly
improved the performance of auto-mated medical image segmentation. In this
paper, we compare severalstate-of-the-art convolutional neural network models
for brain tumourimage segmentation. Based on the ensembled segmentation, we
presenta biophysics-guided prognostic model for patient overall survival
predic-tion which outperforms a data-driven radiomics approach. Our methodwon
the second place of the MICCAI 2019 BraTS Challenge for theoverall survival
prediction.

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The categories of this paper are: hep-th
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
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The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat