DSCA-Net: A depthwise separable convolutional neural network with attention mechanism for medical image segmentation
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Published:2022
Issue:1
Volume:20
Page:365-382
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ISSN:1551-0018
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Container-title:Mathematical Biosciences and Engineering
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language:
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Short-container-title:MBE
Author:
Shan Tong1, Yan Jiayong23, Cui Xiaoyao3, Xie Lijian4
Affiliation:
1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. School of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China 3. Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China 4. Children's Hospital of Shanghai, Shanghai 200062, China
Abstract
<abstract>
<p>Accurate segmentation is a basic and crucial step for medical image processing and analysis. In the last few years, U-Net, and its variants, have become widely adopted models in medical image segmentation tasks. However, the multiple training parameters of these models determines high computation complexity, which is impractical for further applications. In this paper, by introducing depthwise separable convolution and attention mechanism into U-shaped architecture, we propose a novel lightweight neural network (DSCA-Net) for medical image segmentation. Three attention modules are created to improve its segmentation performance. Firstly, Pooling Attention (PA) module is utilized to reduce the loss of consecutive down-sampling operations. Secondly, for capturing critical context information, based on attention mechanism and convolution operation, we propose Context Attention (CA) module instead of concatenation operations. Finally, Multiscale Edge Attention (MEA) module is used to emphasize multi-level representative scale edge features for final prediction. The number of parameters in our network is 2.2 M, which is 71.6% less than U-Net. Experiment results across four public datasets show the potential and the dice coefficients are improved by 5.49% for ISIC 2018, 4.28% for thyroid, 1.61% for lung and 9.31% for nuclei compared with U-Net.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
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