[1]刘雲,王一达,张成秀,等.基于深度学习结合解剖学注意力机制的肺结节良恶性分类[J].中国医学物理学杂志,2022,39(11):1441-1447.[doi:DOI:10.3969/j.issn.1005-202X.2022.11.019]
　LIU Yun,WANG Yida,ZHANG Chengxiu,et al.Classification of benign and malignant pulmonary nodules by deep learning with anatomy-based attention mechanism[J].Chinese Journal of Medical Physics,2022,39(11):1441-1447.[doi:DOI:10.3969/j.issn.1005-202X.2022.11.019]
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基于深度学习结合解剖学注意力机制的肺结节良恶性分类()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第11期
页码:
1441-1447
栏目:
医学人工智能
出版日期:
2022-11-25

文章信息/Info

Title:
Classification of benign and malignant pulmonary nodules by deep learning with anatomy-based attention mechanism
文章编号:
1005-202X（2022）11-1441-07
作者:
刘雲; 王一达; 张成秀; 杨光; 王成龙
华东师范大学物理与电子科学学院上海市磁共振重点实验室， 上海 200062
Author(s):
LIU Yun;  WANG Yida;  ZHANG Chengxiu;  YANG Guang;  WANG Chenglong
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
关键词:
肺结节; 注意力机制; CT图像; 深度学习
Keywords:
Keywords: pulmonary nodule attention mechanism CT image deep learning
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2022.11.019
文献标志码:
A
摘要:
肺结节作为肺癌的初期表现，及时的发现和准确的良恶性诊断对于疾病的治疗具有重要的意义。为了提高肺部CT图像中肺结节良恶性的诊断率，提出一种基于3D ResNet的卷积神经网络，并通过加入解剖学注意力模块有效地提高了肺结节良恶性的分类精度。此外，该方法通过自动分割以获取注意力机制所需的感兴趣区域，实现整个流程的全自动化。解剖学注意力的添加能更好地捕捉图像中的局部纹理信息，进一步提取对于肺结节良恶性诊断有用的特征。本文方法在LIDC-IDRI数据集上进行验证。实验结果表明与传统的3D ResNet及其他现有的方法相比，本文方法在分类精度上有显著的提高，在独立测试集上的最终分类的AUC达到0.973，准确率为0.940。由此可见，本文方法能在辅助医生对肺结节的诊断中起到重要作用。
Abstract:
Abstract: Pulmonary nodule is the initial sign of lung cancer, and the timely detection and accurate diagnosis of malignant and benign nodules have great significance for the treatment of diseases. In order to improve the diagnostic accuracy of benign and malignant pulmonary nodules in pulmonary CT images, a novel convolution neural network based on 3D ResNet is proposed, with anatomy-based attention mechanism for greatly improving the classification accuracy of pulmonary nodules. In addition, the region of interest required for the attention mechanism is obtained by automatic segmentation, thereby achieving the full automation of the whole process. The addition of anatomy-based attention mechanism can better capture local texture information in CT images and further extract useful features for diagnosing benign and malignant pulmonary nodules. The proposed method is verified in LIDC-IDRI data set. The results show that the proposed method greatly improves classification accuracy as compared with other existed methods and traditional 3D ResNet, achieving an area under the receiver operating characteristic curve (AUC) of 0.973 and an accuracy of 94.0% in the independent test set. The proposed method has the potential to assist doctors in the diagnosis of pulmonary nodules.

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备注/Memo

备注/Memo:
【收稿日期】2022-07-13 【基金项目】中国博士后科学基金（2021M691038）；上海市浦江人才计划（2020PJD016） 【作者简介】刘雲，硕士研究生，研究方向：医学图像处理，人工智能，E-mail: yliumri@gmail.com 【通信作者】王成龙，博士，助理研究员，研究方向：医学图像处理、人工智能，E-mail: clwang@phy.ecnu.edu.cn
更新日期/Last Update: 2022-11-25