ArticleJournal of digital imaging2020
Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Artificial Intelligence for the Diagnosis of Respiratory Diseases in Dogs and Cats: A Systematic Review.Veterinary sciences · 2026Review
- A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Review
- Large-scale long-tailed disease diagnosis on radiology images.Nature communications · 2024Article
- Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.Journal of imaging informatics in medicine · 2024Article
- Radiomics-based machine learning for automated detection of Pneumothorax in CT scans.PloS one · 2024Article
- Deep Learning-based Diagnosis and Localization of Pneumothorax on Portable Supine Chest X-ray in Intensive and Emergency Medicine: A Retrospective Study.Journal of medical systems · 2023Article
- "Quo Vadis Diagnosis": Application of Informatics in Early Detection of Pneumothorax.Diagnostics (Basel, Switzerland) · 2023Article
- Performance and Usability of Code-Free Deep Learning for Chest Radiograph Classification, Object Detection, and Segmentation.Radiology. Artificial intelligence · 2023Article
- Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification.Radiology. Artificial intelligence · 2023Article
- The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.Journal of digital imaging · 2023Article
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
- Chest L-Transformer: Local Features With Position Attention for Weakly Supervised Chest Radiograph Segmentation and Classification.Frontiers in medicine · 2022Article
- Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese.PloS one · 2022Article
- Curation of the CANDID-PTX Dataset with Free-Text Reports.Radiology. Artificial intelligence · 2021Article
- Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training.European radiology · 2021Article
- Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process.Scientific reports · 2021Article
- U-shaped GAN for Semi-Supervised Learning and Unsupervised Domain Adaptation in High Resolution Chest Radiograph Segmentation.Frontiers in medicine · 2021Article
- Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density.Journal of the American College of Radiology : JACR · 2020Article
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Authors and funding
20 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Pneumothorax is a potentially life-threatening condition that requires prompt recognition and often urgent intervention. In the ICU setting, large numbers of chest radiographs are performed and must be interpreted on a daily basis which may delay diagnosis of this entity. Development of artificial intelligence (AI) techniques to detect pneumothorax could help expedite detection as well as localize and potentially quantify pneumothorax. Open image analysis competitions are useful in advancing state-of-the art AI algorithms but generally require large expert annotated datasets. We have annotated and adjudicated a large dataset of chest radiographs to be made public with the goal of sparking innovation in this space. Because of the cumbersome and time-consuming nature of image labeling, we explored the value of using AI models to generate annotations for review. Utilization of this machine learning annotation (MLA) technique appeared to expedite our annotation process with relatively high sensitivity at the expense of specificity. Further research is required to confirm and better characterize the value of MLAs. Our adjudicated dataset is now available for public consumption in the form of a challenge.
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