Update README.md

README.md

@@ -1,73 +1,78 @@
+---
 license: cc-by-4.0
-pretty_name: X-ray Reports Dataset
+pretty_name: 'X-ray Reports Dataset'
 language:
-  - en
+- en
 tags:
-  - medical
-  - x-ray
-  - radiology
-  - chest
-  - reports
-  - image-to-text
-  - medical-imaging
-  - ai-research
+- medical
+- x-ray
+- radiology
+- chest
+- reports
+- image-to-text
+- medical-imaging
+- ai-research
 task_categories:
-  - image-classification
-  - image-text-to-text
+- image-classification
+- image-text-to-text
 size_categories:
-  - 10K<n<100K
-
-dataset_name: X-ray Reports Dataset
-description: |
-  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:
-  - anoushka@kgen.io
-  - abhishek.vadapalli@kgen.io
-
-supported_tasks:
-  task_categories:
-    - Image Classification
-    - Image-to-Text Generation
-  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: 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.
-
-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
+- 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