文献类型：期刊文献

英文题名：Pulmonary nodule detection based on Hierarchical-Split HRNet and feature pyramid network with atrous convolution

作者：Zhu, Ling[1];Zhu, Hongqing[1];Yang, Suyi[2];Wang, Pengyu[1];Huang, Hui[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Kings Coll London, Dept Math Nat Math & Engn Sci, London WC2R 2LS, England;[3]Shanghai Normal Univ, Coll Informat & Elect Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：85

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20232214154263);WOS:【SCI-EXPANDED(收录号:WOS:001010846500001)】；

基金：Acknowledgments The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments that significantly improved the quality of this paper. This work was supported by the National Nature Science Foundation of China under Grant 61872143.

语种：英文

外文关键词：Pulmonary nodule detection; Hierarchical-Split Block; High-resolution network; Feature pyramid network; Adversarial training

摘要：Accurate pulmonary nodule detection is crucial to the diagnosis of lung diseases. In this work, we propose an end-to-end network for pulmonary nodule detection mainly consisting of pre-processing, detection modules for candidate prediction, and a discriminator to identify the existence of nodules. In the detection module, HS-HRNet is proposed to fulfill feature extraction on high-resolution input for pulmonary nodules that occupy tiny spaces of CT images. HS-HRNet incorporates plug-and-play Hierarchical-Split block into High-Resolution Network (HRNet) and modifies STEM with sandglass module. The main advantages of these modifications are that HS-HRNet can largely promote feature representation ability by split and concatenation operation with no significant increase in computation. In addition, a novel Feature Pyramid Network with Atrous Convolution (AC-FPN) is proposed for multi-scale feature fusion and multi-level prediction. This design allows context and spatial information in different feature levels to be leveraged and extracted under larger receptive fields. Besides, a discriminator replaces false positive reduction modules in most pulmonary detection methods. The discriminator judges the existence of nodules and back-propagates prediction proposals to detection modules through adversarial training. Experiments on publicly available datasets demonstrate competitive performance in pulmonary nodule detection.