Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting Language Recovery after Stroke with Convolutional Networks on
  Stitched MRI   One third of stroke survivors have language difficulties. Emerging evidence
suggests that their likelihood of recovery depends mainly on the damage to
language centers. Thus previous research for predicting language recovery
post-stroke has focused on identifying damaged regions of the brain. In this
paper, we introduce a novel method where we only make use of stitched
2-dimensional cross-sections of raw MRI scans in a deep convolutional neural
network setup to predict language recovery post-stroke. Our results show: a)
the proposed model that only uses MRI scans has comparable performance to
models that are dependent on lesion specific information; b) the features
learned by our model are complementary to the lesion specific information and
the combination of both appear to outperform previously reported results in
similar settings. We further analyse the CNN model for understanding regions in
brain that are responsible for arriving at these predictions using gradient
based saliency maps. Our findings are in line with previous lesion studies.

4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
19
The categories of this paper are: eess
3
The categories of this paper are: physics
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th