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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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