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作者:

Yuan, Haiying (Yuan, Haiying.) | Wu, Yanrui (Wu, Yanrui.) | Cheng, Junpeng (Cheng, Junpeng.) | Fan, Zhongwei (Fan, Zhongwei.) | Zeng, Zhiyong (Zeng, Zhiyong.)

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EI Scopus SCIE

摘要:

Accurate detection of pulmonary nodules on chest computed tomography scans is crucial to early diagnosis of lung cancer. To address the thorn problems on low detection sensitivity and high false-positive rate caused by heterogeneity and morphological complexity of 3-D nodule features, a computer-aided detection system is developed to increase the detection sensitivity and classification accuracy of pulmonary nodules. The contributions include: (1) Nodule candidate detection: 3-D Residual U-Net model is improved to detect candidate nodules, which constructs 3-D context-guided module to extract local and global nodule features by setting the dilated convolution with different dilation rates. Furthermore, channel attention mechanism is used to dynamically adjust the channel features, which enhances the generalization and expression ability of the detection-network to effectively learn 3-D spatial context features. (2) False-positive reduction: multi-branch classification network is designed for multi-task learning. Image reconstruction task is performed to retain more microscopic nodules information from convolutional neural network (CNN) hierarchy. Moreover, each branch deals with the feature map at corresponding depth layers, and then all branches' feature maps are combined together to perform nodule classification task. Numerous experimental results show that the proposed system is perfectly qualified for pulmonary nodules detection on Lung Nodules Analysis 2016 dataset, which achieves detection sensitivity up to 94.0% and competition performance metric (CPM) score up to 0.959.

关键词:

multi-branch classification convolutional neural network Computed tomography Lung Lung cancer Tumors 3-D context-guided attention module multi-task learning Convolutional neural networks Feature extraction Three-dimensional displays pulmonary nodule detection

作者机构:

  • [ 1 ] [Yuan, Haiying]Beijing Univ Technol, Fac Informat Technol, Beijing 100020, Peoples R China
  • [ 2 ] [Wu, Yanrui]Beijing Univ Technol, Fac Informat Technol, Beijing 100020, Peoples R China
  • [ 3 ] [Cheng, Junpeng]Beijing Univ Technol, Fac Informat Technol, Beijing 100020, Peoples R China
  • [ 4 ] [Fan, Zhongwei]Beijing Univ Technol, Fac Informat Technol, Beijing 100020, Peoples R China
  • [ 5 ] [Zeng, Zhiyong]Beijing Univ Technol, Fac Informat Technol, Beijing 100020, Peoples R China

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来源 :

IEEE ACCESS

ISSN: 2169-3536

年份: 2022

卷: 10

页码: 82-98

3 . 9

JCR@2022

3 . 9 0 0

JCR@2022

JCR分区:2

中科院分区:3

被引次数:

WoS核心集被引频次: 13

SCOPUS被引频次: 17

ESI高被引论文在榜: 0 展开所有

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