Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Left ventricle quantification through spatio-temporal CNNs   Cardiovascular diseases are among the leading causes of death globally.
Cardiac left ventricle (LV) quantification is known to be one of the most
important tasks for the identification and diagnosis of such pathologies. In
this paper, we propose a deep learning method that incorporates 3D
spatio-temporal convolutions to perform direct left ventricle quantification
from cardiac MR sequences. Instead of analysing slices independently, we
process stacks of temporally adjacent slices by means of 3D convolutional
kernels which fuse the spatio-temporal information, incorporating the temporal
dynamics of the heart to the learned model. We show that incorporating such
information by means of spatio-temporal convolutions into standard LV
quantification architectures improves the accuracy of the predictions when
compared with single-slice models, achieving competitive results for all
cardiac indices and significantly breaking the state of the art (Xue et al.,
2018, MedIA) for cardiac phase estimation.

21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
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The categories of this paper are: eess
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph