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Towards automatic pulmonary nodule management in lung cancer screening with deep learning

https://doi.org/10.1038/srep46479
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A Hybrid Multi-Model Deep Learning and Ensemble Framework for Automated Lung Cancer Detection Using CT Imaging
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Segmentation and Classification of 3D Lung Tumor Diagnoses Using Convolutional Neural Networks
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Enhanced Breast and Lung Cancer Classification Using a Multi-Task Deep Learning Framework Based on Swin Transformer
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A Multi-Task, Multi-Model Deep Learning Analysis for Lung Cancer Detection on CT Scans
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Performance Evaluation and Comparative Analysis of a Hybrid Intelligence Framework for Multiclass Lung Cancer Classification
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Robust Lung Cancer Detection Across Diverse Low-Dose CT using Domain-Generalized Deep Learning
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Explainable Deep Learning for Automated Lung Nodule Classification: Integrating Googlenet Architecture with Grad-CAM Visualization on LUNA16 Dataset
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An Improved Multiscale Faster R-CNN Based Lung Cancer Detection System
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Lung Cancer Diagnosis and Classification Using Hybrid Neural Network Techniques
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Classification of Lung Cancer Using Ensemble Deep Neural Network Through Computed Tomography Images
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Impact of Image Preprocessing Methods on Deep Learning Efficiency for Lung Cancer Diagnosis from CT Scans
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3-D Convolutional Neural Networks for Automatic Detection of Pulmonary Nodules in Chest CT
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A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
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From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities
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Lung Nodule Detection via 3D U-Net and Contextual Convolutional Neural Network
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Applying Deep Learning and Natural Language Processing in Cancer: A Survey
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Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models
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A Comprehensive Review of Machine Learning Techniques for Lung Cancer Detection
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MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer
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Characterization of Pulmonary Nodules in Computed Tomography Images Based on Pseudo-Labeling Using Radiology Reports
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COVID-19 Lung Infection Segmentation With Cascaded Aggregation Network
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Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches
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Dual Convolutional Neural Networks for Breast Mass Segmentation and Diagnosis in Mammography
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Multi-Label Softmax Networks for Pulmonary Nodule Classification Using Unbalanced and Dependent Categories
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Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging
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3D ARCNN: An Asymmetric Residual CNN for False Positive Reduction in Pulmonary Nodule
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Application of artificial intelligence in lung cancer screening: A real‐world study in a Chinese physical examination population
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Dose robustness of deep learning models for anatomic segmentation of computed tomography images
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Computer-aided CT image features improving the malignant risk prediction in pulmonary nodules suspicious for lung cancer
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Machine learning in GI endoscopy: practical guidance in how to interpret a novel field
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The Detection and Recognition of Pulmonary Nodules Based on U-net and CNN
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Using Computer Analysis to Predict Likelihood of Cancer in Lung Nodules
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Current Applications and Future Impact of Machine Learning in Radiology
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Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
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Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks
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Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT
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Evaluating a Fully Automated Pulmonary Nodule Detection Approach and Its Impact on Radiologist Performance
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Artificial Intelligence for Classification of Soft-Tissue Masses at US
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Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning
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Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes
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Characterizing the Impact of Training Data on Generalizability: Application in Deep Learning to Estimate Lung Nodule Malignancy Risk
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[Retracted] Detection and Classification of Colorectal Polyp Using Deep Learning
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Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models
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Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network
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Development and Validation of Machine Learning–based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts
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Current Evidence for a Lung Cancer Screening Program
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Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules
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Lung cancer diagnosis with GAN supported deep learning models
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Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database
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Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks
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Primary Care Provider Preferences Regarding Artificial Intelligence in Point-of-Care Cancer Screening
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Unravelling machine learning: insights in respiratory medicine
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Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening
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Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19
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Differentiating benign and malignant solitary pulmonary nodules through exhaled breath analysis
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ERS International Congress 2023: highlights from the Clinical Techniques, Imaging and Endoscopy Assembly
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Development and performance evaluation of a deep learning lung nodule detection system
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The value of deep learning-based computer aided diagnostic system in improving diagnostic performance of rib fractures in acute blunt trauma
inferred← citeshttps://doi.org/10.1186/s12916-021-01928-3openalex-snapshot
Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value
inferred← citeshttps://doi.org/10.1186/s12938-018-0496-2openalex-snapshot
Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder
inferred← citeshttps://doi.org/10.1186/s12938-020-00793-0openalex-snapshot
Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference
inferred← citeshttps://doi.org/10.1186/s41747-019-0109-2openalex-snapshot
Machine learning applications in prostate cancer magnetic resonance imaging
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Artificial Intelligence Approach for Variant Reporting
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European lung cancer screening: valuable trial evidence for optimal practice implementation
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Application of deep learning image reconstruction in low-dose chest CT scan
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The lung cancers: staging and response, CT, 18F-FDG PET/CT, MRI, DWI: review and new perspectives
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Research Progress on Artificial Intelligence-Based Pulmonary Nodule Detection
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Evaluating reproducibility of AI algorithms in digital pathology with DAPPER
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Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
inferred← citeshttps://doi.org/10.1371/journal.pone.0200721openalex-snapshot
Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning
inferred← citeshttps://doi.org/10.1371/journal.pone.0300325openalex-snapshot
Development of automatic generation system for lung nodule finding descriptions
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