ArticleJournal of digital imaging2019
Generalizable Inter-Institutional Classification of Abnormal Chest Radiographs Using Efficient Convolutional Neural Networks.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Cross-Domain Chest Radiograph Report Generation Under Dataset Shift: Bidirectional Audit Study.JMIR medical informatics · 2026Article
- Highly Accurate Occupational Pneumoconiosis Staging via Dark Channel Prior-Inspired Lesion Area Enhancement.Journal of imaging informatics in medicine · 2025Article
- Evaluating the robustness of deep learning models trained to diagnose idiopathic pulmonary fibrosis using a retrospective study.Medical physics · 2025Article
- Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy.Radiation oncology (London, England) · 2024Article
- A retrospective study of deep learning generalization across two centers and multiple models of X-ray devices using COVID-19 chest-X rays.Scientific reports · 2024Article
- Clinical applications of artificial intelligence in radiology.The British journal of radiology · 2023Review
- CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model.Diagnostics (Basel, Switzerland) · 2023Article
- The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.Radiology. Artificial intelligence · 2023Article
- A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images.Diagnostics (Basel, Switzerland) · 2022Review
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review.Radiology. Artificial intelligence · 2022Article
- AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease.Frontiers in medicine · 2022Article
- Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs.Journal of digital imaging · 2021Article
- Development and multicenter validation of chest X-ray radiography interpretations based on natural language processing.Communications medicine · 2021Article
- Lessons learned in transitioning to AI in the medical imaging of COVID-19.Journal of medical imaging (Bellingham, Wash.) · 2021Review
- CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network.Computational and mathematical methods in medicine · 2021Article
- A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells).Expert review of molecular diagnostics · 2020Article
- Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19.Machine vision and applications · 2020Article
- Artificial Intelligence in Imaging: The Radiologist's Role.Journal of the American College of Radiology : JACR · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our objective is to evaluate the effectiveness of efficient convolutional neural networks (CNNs) for abnormality detection in chest radiographs and investigate the generalizability of our models on data from independent sources. We used the National Institutes of Health ChestX-ray14 (NIH-CXR) and the Rhode Island Hospital chest radiograph (RIH-CXR) datasets in this study. Both datasets were split into training, validation, and test sets. The DenseNet and MobileNetV2 CNN architectures were used to train models on each dataset to classify chest radiographs into normal or abnormal categories; models trained on NIH-CXR were designed to also predict the presence of 14 different pathological findings. Models were evaluated on both NIH-CXR and RIH-CXR test sets based on the area under the receiver operating characteristic curve (AUROC). DenseNet and MobileNetV2 models achieved AUROCs of 0.900 and 0.893 for normal versus abnormal classification on NIH-CXR and AUROCs of 0.960 and 0.951 on RIH-CXR. For the 14 pathological findings in NIH-CXR, MobileNetV2 achieved an AUROC within 0.03 of DenseNet for each finding, with an average difference of 0.01. When externally validated on independently collected data (e.g., RIH-CXR-trained models on NIH-CXR), model AUROCs decreased by 3.6-5.2% relative to their locally trained counterparts. MobileNetV2 achieved comparable performance to DenseNet in our analysis, demonstrating the efficacy of efficient CNNs for chest radiograph abnormality detection. In addition, models were able to generalize to external data albeit with performance decreases that should be taken into consideration when applying models on data from different institutions.
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