Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-end learning of brain tissue segmentation from imperfect labeling   Segmenting a structural magnetic resonance imaging (MRI) scan is an important
pre-processing step for analytic procedures and subsequent inferences about
longitudinal tissue changes. Manual segmentation defines the current gold
standard in quality but is prohibitively expensive. Automatic approaches are
computationally intensive, incredibly slow at scale, and error prone due to
usually involving many potentially faulty intermediate steps. In order to
streamline the segmentation, we introduce a deep learning model that is based
on volumetric dilated convolutions, subsequently reducing both processing time
and errors. Compared to its competitors, the model has a reduced set of
parameters and thus is easier to train and much faster to execute. The contrast
in performance between the dilated network and its competitors becomes obvious
when both are tested on a large dataset of unprocessed human brain volumes. The
dilated network consistently outperforms not only another state-of-the-art deep
learning approach, the up convolutional network, but also the ground truth on
which it was trained. Not only can the incredible speed of our model make large
scale analyses much easier but we also believe it has great potential in a
clinical setting where, with little to no substantial delay, a patient and
provider can go over test results.

27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
32
The categories of this paper are: atom-ph