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X-ray Reports Dataset

This dataset contains high-quality (“A-grade”) anonymized X-ray images paired with radiology reports. It has been carefully curated, cleaned, and verified to ensure accuracy, completeness, and compliance with privacy standards (e.g., HIPAA/GDPR), making it suitable for high-stakes or research-grade model training.

Contact

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Supported Tasks

  • Task Categories:

    • Image Classification
    • Image-to-Text Generation
  • Supported Tasks:

    • Radiology report generation from X-ray images
    • Multi-label classification of thoracic pathologies (e.g., pneumonia, cardiomegaly)
    • Medical image analysis for triage support
    • Cross-modal learning for vision-language models
    • Feature extraction for diagnostic AI research

Languages

  • Primary Language: English (radiology reports)

Dataset Creation

Curation Rationale

This dataset was created to advance medical AI research by providing paired X-ray images and radiology reports for tasks like automated report generation and disease detection. It aims to support the development of robust, generalizable models for radiology.

Source Data

  • Contributors: De-identified data from hospital archives and public medical repositories
  • Collection Process: Images sourced from PACS systems (2015–2023), reports authored by board-certified radiologists, anonymized to remove patient identifiers.

Other Known Limitations

  • Size: Limited to ~10,000 samples, which may restrict generalization
  • Demographic Bias: Overrepresentation of adult urban patients; limited pediatric data
  • Image Quality: Variations in X-ray resolution or equipment may affect consistency
  • Label Noise: Potential errors in report-based labels extracted via NLP

Intended Uses

✅ Direct Use

  • Training and benchmarking models for radiology report generation
  • Research in medical image-to-text generation
  • Development of AI tools for radiology triage and decision support
  • Academic research in medical imaging and natural language processing

❌ Out-of-Scope Use

  • Clinical diagnosis without human radiologist oversight
  • Commercial use without proper attribution or ethical review
  • Applications violating patient privacy or medical ethics
  • Real-time deployment without additional validation

License

CC BY 4.0

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