Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MA-Unet: An improved version of Unet based on multi-scale and attention
  mechanism for medical image segmentation   Although convolutional neural networks (CNNs) are promoting the development
of medical image semantic segmentation, the standard model still has some
shortcomings. First, the feature mapping from the encoder and decoder
sub-networks in the skip connection operation has a large semantic difference.
Second, the remote feature dependence is not effectively modeled. Third, the
global context information of different scales is ignored. In this paper, we
try to eliminate semantic ambiguity in skip connection operations by adding
attention gates (AGs), and use attention mechanisms to combine local features
with their corresponding global dependencies, explicitly model the dependencies
between channels and use multi-scale predictive fusion to utilize global
information at different scales. Compared with other state-of-the-art
segmentation networks, our model obtains better segmentation performance while
introducing fewer parameters.

23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg