ArticleJournal of digital imaging2023
The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence competitions in radiology: a focus on design, evaluation, and trends.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Pooled it
- Chest X-Ray Foundation Model With Global and Local Representations Integration.IEEE transactions on medical imaging · 2025Article
- Explained Deep Learning Framework for COVID-19 Detection in Volumetric CT Images Aligned with the British Society of Thoracic Imaging Reporting Guidance: A Pilot Study.Journal of imaging informatics in medicine · 2025Article
- CXR-LLaVA: a multimodal large language model for interpreting chest X-ray images.European radiology · 2025Article
- Review
- Improving Fairness of Automated Chest Radiograph Diagnosis by Contrastive Learning.Radiology. Artificial intelligence · 2024Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data.Bioengineering (Basel, Switzerland) · 2023Article
- Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.Scientific reports · 2023Article
- Improving model fairness in image-based computer-aided diagnosis.Nature communications · 2023Article
- Data drift in medical machine learning: implications and potential remedies.The British journal of radiology · 2023Review
- Leveraging human expert image annotations to improve pneumonia differentiation through human knowledge distillation.Scientific reports · 2023Article
- Automatic Identification of Lung Opacities Due to COVID-19 from Chest X-ray Images-Focussing Attention on the Lungs.Diagnostics (Basel, Switzerland) · 2023Article
- Dark-field chest X-ray imaging for the assessment of COVID-19-pneumonia.Communications medicine · 2022Article
- Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
29 authors.
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Abstract
We describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including "typical," "indeterminate," and "atypical appearance" for COVID-19, or "negative for pneumonia," adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use.
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