Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Task Learning for Left Atrial Segmentation on GE-MRI   Segmentation of the left atrium (LA) is crucial for assessing its anatomy in
both pre-operative atrial fibrillation (AF) ablation planning and
post-operative follow-up studies. In this paper, we present a fully automated
framework for left atrial segmentation in gadolinium-enhanced magnetic
resonance images (GE-MRI) based on deep learning. We propose a fully
convolutional neural network and explore the benefits of multi-task learning
for performing both atrial segmentation and pre/post ablation classification.
Our results show that, by sharing features between related tasks, the network
can gain additional anatomical information and achieve more accurate atrial
segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI
images. Code of our proposed algorithm is available at
https://github.com/cherise215/atria_segmentation_2018/.

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The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys