README.md

metadata

license: cc-by-4.0
pretty_name: X-ray Reports Dataset
language:
  - en
tags:
  - medical
  - x-ray
  - radiology
  - chest
  - reports
  - image-to-text
  - medical-imaging
  - ai-research
task_categories:
  - image-classification
  - image-text-to-text
size_categories:
  - 10K<n<100K

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

For queries or collaborations related to this dataset, contact:

• anoushka@kgen.io
• abhishek.vadapalli@kgen.io

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