Serial attention network for skin lesion segmentation

被引：0
作者：
Yuan Ren
Long Yu
Shengwei Tian
Junlong Cheng
Zhiqi Guo
Yanhan Zhang
机构：
[1] Xinjiang University,College of Software
[2] Xinjiang University,Key Laboratory of Software Engineering Technology
[3] Xinjiang University,Network Center
[4] Xinjiang University,College of Information Science and Engineering
来源：
Journal of Ambient Intelligence and Humanized Computing | 2022年 / 13卷
关键词：
Spatial attention; Channel attention; Skin lesion segmentation; Deep learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Due to the influence of color, boundaries, and shapes of melanoma, the segmentation of the lesion area is still a challenging problem. In this paper, we propose a method to connect two attention modules (channel attention and spatial attention) serially and embed them into the skip connection of the encoder–decoder network. Different from previous work on image segmentation through attention mechanism, we have made a different combination of the existing popular spatial attention module and channel attention module. The experimental results show that the sequence combinations with channel attention in front and spatial attention in the rear are more likely to aggregate global and local information as well as information between channels than other combinations. Our method achieved an average Jaccard Index of 0.7692 on the ISIC2017 dataset. At the same time, we also compared with some advanced methods of image segmentation, the experimental results show our proposed method has a competitive performance.
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页码：799 / 810
页数：11
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[1]  
Badrinarayanan V(2017)SegNet: a deep convolutional encoder-decoder architecture for image segmentation IEEE Trans Pattern Anal Mach Intell 39 2481-2495
[2]  
Kendall A(2017)Dermoscopic image segmentation via multistage fully convolutional networks IEEE Trans Biomed Eng 64 2065-2074
[3]  
Cipolla R(2018)DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs IEEE Trans Pattern Anal Mach Intell 40 834-848
[4]  
Bi L(2018)Social group optimization supported segmentation and evaluation of skin melanoma images Symmetry 168 11-19
[5]  
Kim J(2019)Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding Comput Methods Programs Biomed 38 2281-2292
[6]  
Ahn E(2019)CE-Net: context encoder network for 2D medical image segmentation IEEE Trans Med Imaging 37 1904-1916
[7]  
Kumar A(2015)Spatial pyramid pooling in deep convolutional networks for visual recognition IEEE Trans Pattern Anal Mach Intell 42 2011-2023
[8]  
Fulham M(2020)Squeeze-and-excitation networks IEEE Trans Pattern Anal Mach Intell 6 1-16
[9]  
Feng D(2018)Skin lesion analysis towards melanoma detection using deep learning network Sensors 3 35-45
[10]  
Chen L(2019)Recurrent residual U-Net for medical image segmentation J Med Imaging 15 929-936
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