ArticlePloS one2017
3D multi-view convolutional neural networks for lung nodule classification.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.Journal of medical systems · 2019Pooled it
- Predicting Recurrence Risk of Glioblastoma Based on Preoperative-Postoperative Longitudinal MRI: A Multicenter Study.Bioengineering (Basel, Switzerland) · 2026Article
- An intelligent lung nodule classification model using 3D Trans-DenseUnet++-based lung nodule segmentation.Scientific reports · 2026Article
- Interpretable Deep Learning with Multi-Scale CT for Predicting Occult Lymph Node Metastasis in Early-Stage NSCLC: A Multicenter Study.Journal of imaging informatics in medicine · 2026Article
- Voxel Normalization in LDCT Imaging: Its Significance in Texture Feature Selection for Pulmonary Nodule Malignancy Classification: Insights from Two Centers.Diagnostics (Basel, Switzerland) · 2026Article
- Segmentation-Guided Hybrid Deep Learning for Pulmonary Nodule Detection and Risk Prediction from Multi-Cohort CT Images.Diseases (Basel, Switzerland) · 2026Article
- Multi-view attention-based deep learning for benign-malignant classification of pulmonary nodules.PloS one · 2026Article
- A deep learning model to enhance lung cancer detection using 'Dual-Branch' model classification approach.PloS one · 2026Article
- Thoracic staging of lung cancers byPhysical and engineering sciences in medicine · 2025Article
- A novel artificial intelligence segmentation model for early diagnosis of bladder tumors.Abdominal radiology (New York) · 2025Article
- A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer.Indian journal of surgical oncology · 2025Review
- An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.Thoracic cancer · 2024Article
- Are deep learning classification results obtained on CT scans fair and interpretable?Physical and engineering sciences in medicine · 2024Article
- Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer.Journal of translational medicine · 2024Article
- Predicting epidermal growth factor receptor mutation status of lung adenocarcinoma based on PET/CT images using deep learning.Frontiers in oncology · 2024Article
- Experimental Examination of Conventional, Semi-Automatic, and Automatic Volumetry Tools for Segmentation of Pulmonary Nodules in a Phantom Study.Diagnostics (Basel, Switzerland) · 2023Article
- Impact of Voxel Normalization on a Machine Learning-Based Method: A Study on Pulmonary Nodule Malignancy Diagnosis Using Low-Dose Computed Tomography (LDCT).Diagnostics (Basel, Switzerland) · 2023Article
- Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis.British journal of cancer · 2023Article
- A novel fusion algorithm for benign-malignant lung nodule classification on CT images.BMC pulmonary medicine · 2023Article
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4 authors.
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Abstract
The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy.
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