Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient
  CNN for Medical Images Segmentation   Recently, deep learning has become much more popular in computer vision area.
The Convolution Neural Network (CNN) has brought a breakthrough in images
segmentation areas, especially, for medical images. In this regard, U-Net is
the predominant approach to medical image segmentation task. The U-Net not only
performs well in segmenting multimodal medical images generally, but also in
some tough cases of them. However, we found that the classical U-Net
architecture has limitation in several aspects. Therefore, we applied
modifications: 1) designed efficient CNN architecture to replace encoder and
decoder, 2) applied residual module to replace skip connection between encoder
and decoder to improve based on the-state-of-the-art U-Net model. Following
these modifications, we designed a novel architecture--DC-UNet, as a potential
successor to the U-Net architecture. We created a new effective CNN
architecture and build the DC-UNet based on this CNN. We have evaluated our
model on three datasets with tough cases and have obtained a relative
improvement in performance of 2.90%, 1.49% and 11.42% respectively compared
with classical U-Net. In addition, we used the Tanimoto similarity to replace
the Jaccard similarity for gray-to-gray image comparisons.

16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys