---
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