[1]张琼,杭益柳,邱建林,等.高效注意力金字塔网络在肺结节检测的应用[J].中国医学物理学杂志,2024,41(11):1361-1369.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.007]
　ZHANG Qiong,HANG Yiliu,et al.Efficient attention feature pyramid network for pulmonary nodule detection[J].Chinese Journal of Medical Physics,2024,41(11):1361-1369.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.007]
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高效注意力金字塔网络在肺结节检测的应用()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第11期
页码:
1361-1369
栏目:
医学影像物理
出版日期:
2024-11-26

文章信息/Info

Title:
Efficient attention feature pyramid network for pulmonary nodule detection
文章编号:
1005-202X（2024）11-1361-09
作者:
张琼1; 2; 杭益柳1; 2; 邱建林1; 2; 3; 吴芳1; 2
1.南通理工学院计算机与信息工程学院， 江苏 南通 226000； 2.南通市虚拟现实与云计算重点实验室， 江苏 南通 226000； 3.南通大学信息科学技术学院， 江苏 南通 226000
Author(s):
ZHANG Qiong1;  2;  HANG Yiliu1;  2;  QIU Jianlin1;  2;  3;  WU Fang1;  2
1. School of Computer and Information Engineering, Nantong Institute of Technology, Nantong 226000, China 2. Nantong Key Laboratory of Virtual Reality and Cloud Computing, Nantong 226000, China 3. School of Information Science and Technology, Nantong University, Nantong 226000, China
关键词:
肺结节; 深度可分离卷积神经网络; 注意力机制; 特征金字塔; 目标检测
Keywords:
Keywords: pulmonary nodule depthwise separable convolutional neural network attention mechanism feature pyramid object detection
分类号:
R318；TP391.4
DOI:
DOI:10.3969/j.issn.1005-202X.2024.11.007
文献标志码:
A
摘要:
针对肺部CT图像结节特征不明确和检测困难的问题，提出一种高效注意力特征金字塔网络。首先，该网络以多尺度特征融合的特征金字塔为主干网络，保留丰富的低层特征和高层特征，同时采用深度可分离卷积神经网络提取特征信息；然后，将注意力机制融入主干网络中，对重要特征信息进行加权处理；最后，将所提算法应用在Lung-PET-CT-Dx和Luna16数据集中。实验结果表明，本文算法的精度、召回率和mAP值均优于现有对比算法，证明本文算法在肺结节检测的优越性。
Abstract:
Abstract: To address the challenge of unclear features and difficulties in pulmonary nodule CT image detection, an efficient attention feature pyramid network is proposed. The network firstly employs a feature pyramid of multi-scale feature fusion as the backbone network for effectively preserving both low- and high-level features, and uses the depthwise separable convolutional neural network to extract feature information. Then, the attention mechanism is integrated into the backbone network for assigning weights to salient feature information. Finally, the proposed algorithm is applied to Lung-PET-CT-Dx dataset and Luna16 dataset, and the experimental results demonstrate that the proposed algorithm has higher precision, recall rate and mAP value than the existing comparative algorithms, substantiating its superiority in pulmonary nodule detection.

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备注/Memo

备注/Memo:
【收稿日期】2024-06-24 【基金项目】江苏省自然科学基金（BK20231337）；江苏省高校重大自然科学基金（21KJA510004）；南通市科技局基础科学研究项目（JCZ2022108，MSZ2022161）；南通理工学院中青年骨干教师项目（ZQNGGJS202237，ZQNGGJS202234）；南通理工学院科研项目（2022XK（Z）19） 【作者简介】张琼，博士研究生，讲师，研究方向：深度学习、迁移学习，E-mail: zhangq@ntit.edu.cn 【通信作者】杭益柳，硕士，讲师，研究方向：深度学习、数字图像处理，E-mail: 18862928527@163.com
更新日期/Last Update: 2024-11-26