English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Automatic detection of pulmonary nodules is critical for the early diagnosis and prevention of lung cancer. Computed tomography (CT) is an effective and economical lung cancer detection method. In CT images, the size and shape of pulmonary nodules appear different, and some nodules appear similar to the surrounding tissues. Therefore, the automatic localization of pulmonary nodules in CT images is a challenging task. An attention-embedded three-dimensional convolutional neural network is proposed for pulmonary nodule detection in the current study. Specifically, 1) channel-spatial attention guides 3D ResNet to down sample the input 3D CT patch. The channel pays attention to important features and the space to the region of interest. The two form a complementary feature extraction mechanism to effectively help the global flow of information in the network and refine the feature mapping to extract the nodule context features. 2) The channel-spatial attention module changes the fusion model of the feature pyramid, adaptively adjusts the pixel-level weight between features and extracts multi-scale representative node features. 3) The deep separable convolution is used to replace the standard convolution of ResNet, reducing the time cost and improving the efficiency of model training on the premise of ensuring the model&#x2019;s performance. 4) To adapt the distribution of nodule scale, different characteristic layers correspond to two sizes of anchors. Under the condition of ensuring the detection rate of nodules, the number of anchor frames is reduced, and the network sensitivity is improved. Finally, several ablation experiments are carried out using the LUNA16 dataset. The results revealed that the attention-guided network could extract the multi-scale representative features of nodules, and the average sensitivity was 97.7&#x0025;. Additionally, the CMP score reached 0.912. The extensive experiments demonstrate that the proposed approach can effectively improve the detection sensitivity and control the number of false positive nodules, which has clinical application value and a certain reference value.

Attention-Guided Feature Extraction and Multiscale Feature Fusion 3D ResNet for Automated Pulmonary Nodule Detection