Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Adaptive 2D-3D Ensemble of Fully Convolutional Networks for Medical
  Image Segmentation   Segmentation is a critical step in medical image analysis. Fully
Convolutional Networks (FCNs) have emerged as powerful segmentation models
achieving state-of-the-art results in various medical image datasets. Network
architectures are usually designed manually for a specific segmentation task so
applying them to other medical datasets requires extensive experience and time.
Moreover, the segmentation requires handling large volumetric data that results
in big and complex architectures. Recently, methods that automatically design
neural networks for medical image segmentation have been presented; however,
most approaches either do not fully consider volumetric information or do not
optimize the size of the network. In this paper, we propose a novel
self-adaptive 2D-3D ensemble of FCNs for medical image segmentation that
incorporates volumetric information and optimizes both the model's performance
and size. The model is composed of an ensemble of a 2D FCN that extracts
intra-slice information, and a 3D FCN that exploits inter-slice information.
The architectures of the 2D and 3D FCNs are automatically adapted to a medical
image dataset using a multiobjective evolutionary based algorithm that
minimizes both the segmentation error and number of parameters in the network.
The proposed 2D-3D FCN ensemble was tested on the task of prostate segmentation
on the image dataset from the PROMISE12 Grand Challenge. The resulting network
is ranked in the top 10 submissions, surpassing the performance of other
automatically-designed architectures while being considerably smaller in size.

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The categories of this paper are: nlin
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
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The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex