ArticleInternational journal of computer assisted radiology and surgery2019
Automated pulmonary nodule detection in CT images using 3D deep squeeze-and-excitation networks.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 86 citations in OpenAlex.
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- Deep Learning in Thoracic Oncology: Meta-Analytical Insights into Lung Nodule Early-Detection Technologies.Cancers · 2025Review
- Positron emission tomography imaging biomarker and artificial intelligence for the characterization of solitary pulmonary nodule.Frontiers in nuclear medicine · 2025Article
- TiCNet: Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images.Journal of imaging informatics in medicine · 2024Article
- Gaussian filter facilitated deep learning-based architecture for accurate and efficient liver tumor segmentation for radiation therapy.Frontiers in oncology · 2024Article
- A robust model training strategy using hard negative mining in a weakly labeled dataset for lymphatic invasion in gastric cancer.The journal of pathology. Clinical research · 2024Article
- Value of CT diagnostic techniques based on imaging post-processing systems in the early diagnosis and treatment of lung cancer.American journal of translational research · 2024Article
- Multi-Modal Feature Fusion-Based Multi-Branch Classification Network for Pulmonary Nodule Malignancy Suspiciousness Diagnosis.Journal of digital imaging · 2023Article
- TP-DDI: A Two-Pathway Deep Neural Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- Practice toward standardized performance testing of computer-aided detection algorithms for pulmonary nodule.Frontiers in public health · 2022Article
- Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction.Computational and mathematical methods in medicine · 2022Article
- Application of CT Postprocessing Reconstruction Technique in Differential Diagnosis of Benign and Malignant Solitary Pulmonary Nodules and Analysis of Risk Factors.Computational and mathematical methods in medicine · 2022Article
- Design of lung nodules segmentation and recognition algorithm based on deep learning.BMC bioinformatics · 2021Article
- RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning.International journal of computer assisted radiology and surgery · 2021Article
- Screen-detected solid nodules: from detection of nodule to structured reporting.Translational lung cancer research · 2021Review
- Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network.European radiology · 2021Article
- Prediction of EGFR Mutation Status Based onFrontiers in oncology · 2021Article
- 3D Deep Learning on Medical Images: A Review.Sensors (Basel, Switzerland) · 2020Review
- The application of artificial intelligence and radiomics in lung cancer.Precision clinical medicine · 2020Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposePulmonary nodule detection has great significance for early treating lung cancer and increasing patient survival. This work presents a novel automated computer-aided detection scheme for pulmonary nodules based on deep convolutional neural networks (DCNNs).
methodsThe proposed approach employs 3D DCNNs based on squeeze-and-excitation network and residual network (SE-ResNet) for pulmonary nodule candidate detection and false-positive reduction. Specifically, a 3D region proposal network with a U-Net-like structure is designed for detecting pulmonary nodule candidates. For the subsequent false-positive reduction, a 3D SE-ResNet-based classifier is presented to accurately discriminate the true nodules from candidates. The 3D SE-ResNet modules boost the representational power of the network by adaptively recalibrating channel-wise residual feature responses. Both models utilize 3D SE-ResNet modules to learn nodule features effectively and improve nodule detection performance.
resultsOn the public available lung nodule analysis 2016 dataset with 888 scans included, the proposed method reaches high detection sensitivities of 93.6% and 95.7% at one and four false positives per scan, respectively. Meanwhile, the competition performance metric score of 0.904 is achieved. The proposed method has the capability to detect multi-size nodules, especially the extremely small nodules.
conclusionIn this paper, a 3D DCNNs framework based on 3D SE-ResNet modules is proposed to detect pulmonary nodules in chest CT images accurately. Experimental results demonstrate superior effectiveness of the proposed approach in pulmonary nodule detection task.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.