Update README.md

README.md

@@ -1,54 +1,73 @@
----
 license: cc-by-4.0
 pretty_name: X-ray Reports Dataset
+language:
+  - en
 tags:
-- medical
-- x-ray
-- radiology
-- reports
-- medical-imaging
+  - medical
+  - x-ray
+  - radiology
+  - chest
+  - reports
+  - image-to-text
+  - medical-imaging
+  - ai-research
 task_categories:
-- image-text-to-text
+  - image-classification
+  - image-text-to-text
 size_categories:
-- n<1K
----
+  - 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.
 
-X-ray Reports Dataset
+contact:
+  - anoushka@kgen.io
+  - abhishek.vadapalli@kgen.io
 
-This dataset contains high-quality, 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 research-grade model training.
+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
 
-Contact
-For queries or collaborations related to this dataset, contact:
-anoushka@kgen.io
-abhishek.vadapalli@kgen.io
+languages:
+  primary: English (radiology reports)
 
-Task Categories: 
-Image Classification
+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.
 
-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
+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
 
-Dataset Creation
-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 development of robust, generalizable models for radiology.
-
-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
+license: CC BY 4.0