Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Polyp Segmentation via Multi-scale Subtraction Network   More than 90\% of colorectal cancer is gradually transformed from colorectal
polyps. In clinical practice, precise polyp segmentation provides important
information in the early detection of colorectal cancer. Therefore, automatic
polyp segmentation techniques are of great importance for both patients and
doctors. Most existing methods are based on U-shape structure and use
element-wise addition or concatenation to fuse different level features
progressively in decoder. However, both the two operations easily generate
plenty of redundant information, which will weaken the complementarity between
different level features, resulting in inaccurate localization and blurred
edges of polyps. To address this challenge, we propose a multi-scale
subtraction network (MSNet) to segment polyp from colonoscopy image.
Specifically, we first design a subtraction unit (SU) to produce the difference
features between adjacent levels in encoder. Then, we pyramidally equip the SUs
at different levels with varying receptive fields, thereby obtaining rich
multi-scale difference information. In addition, we build a training-free
network "LossNet" to comprehensively supervise the polyp-aware features from
bottom layer to top layer, which drives the MSNet to capture the detailed and
structural cues simultaneously. Extensive experiments on five benchmark
datasets demonstrate that our MSNet performs favorably against most
state-of-the-art methods under different evaluation metrics. Furthermore, MSNet
runs at a real-time speed of $\sim$70fps when processing a $352 \times 352$
image. The source code will be publicly available at
\url{https://github.com/Xiaoqi-Zhao-DLUT/MSNet}. \keywords{Colorectal Cancer
\and Automatic Polyp Segmentation \and Subtraction \and LossNet.}

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The categories of this paper are: eess
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: q-alg