[1]魏坤,沈记全,赵艳梅.MAUNet：用于皮肤病变分割的轻量级模型[J].中国医学物理学杂志,2023,40(5):555-561.[doi:DOI:10.3969/j.issn.1005-202X.2023.05.006]
　WEI Kun,SHEN Jiquan,ZHAO Yanmei.MAUNet: a lightweight model for skin lesion segmentation[J].Chinese Journal of Medical Physics,2023,40(5):555-561.[doi:DOI:10.3969/j.issn.1005-202X.2023.05.006]
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MAUNet：用于皮肤病变分割的轻量级模型()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第5期
页码:
555-561
栏目:
医学影像物理
出版日期:
2023-05-26

文章信息/Info

Title:
MAUNet: a lightweight model for skin lesion segmentation
文章编号:
1005-202X（2023）05-0555-07
作者:
魏坤1; 沈记全1; 赵艳梅2
1.河南理工大学计算机科学与技术学院， 河南 焦作 450000； 2.河南省儿童医院重症监护室， 河南 郑州 460000
Author(s):
WEI Kun1;  SHEN Jiquan1;  ZHAO Yanmei2
1. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 450000, China 2. Intensive Care Unit, Henan Childrens Hospital, Zhengzhou 460000, China
关键词:
医学图像分割; 注意力机制; 皮肤病识别; 轻量级; UNet
Keywords:
Keywords: medical image segmentation attention mechanism recognition of skin disease lightweight UNet
分类号:
R318；TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2023.05.006
文献标志码:
A
摘要:
针对当前深度学习分割算法参数数量多和计算复杂度高的问题，提出了一种融合多种注意力机制的轻量级模型MAUNet用于皮肤病变分割。该模型在UNet网络基础上融合深度可分离卷积和门控注意力机制模块，用于提取全局和局部特征信息；融入外部注意力机制模块来增强样本间的联系；利用空间和通道注意力模块分别提取通道和空间特征。以ISIC2017皮肤病公开数据集作为数据源，改进的UNet模型实现特征提取与分类。与基线模型UNet相比，平均交并比和Dice相似性系数分别提高了2.18%和1.28%，同时参数量和计算复杂度仅为基线模型的2.1%和0.58%。实验结果表明该模型在参数数量平衡性、计算复杂度和分割检测性能上均达到了较好的水平。
Abstract:
Abstract: The current depth learning segmentation algorithm has the problems of numerous parameters and high computational complexity. Therefore, a lightweight algorithm (MAUNet) which combines UNet and multiple attention mechanisms is proposed for skin lesion segmentation. The model integrates depth-wise separable convolution and gated attention mechanism modules on the basis of UNet to extract global and local feature information, adopts the external attention mechanism module to enhance the connection between samples, and uses the spatial and channel attention mechanism modules to extract channel and spatial features. The MAUNet model realizes feature extraction and classification on ISIC2017 skin disease public data set. Compared with the baseline model (UNet), the proposed model increases mIoU and DSC by 2.18% and 1.28% respectively, while reducing the number of parameters and computational complexity which were only 2.1% and 0.58% of the baseline model. The experimental results show that the model can balance the number of parameters, lower the computational complexity and perform well in segmentation and detection.

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备注/Memo

备注/Memo:
【收稿日期】2023-01-10 【基金项目】国家自然科学基金（61972134） 【作者简介】魏坤，硕士，研究方向：图像处理和网络安全，E-mail: weikunzz@qq.com 【通信作者】沈记全，博士，教授，研究方向：云计算和网格计算，E-mail: sjq@pu.edu.cn
更新日期/Last Update: 2023-05-26