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README.md

@@ -1,16 +1,38 @@
 ---
+language:
+- en
+license: mit
+task_categories:
+- visual-question-answering
+- image-classification
+task_ids:
+- medical-vqa
+- malaria-detection
+- parasite-classification
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- medical
+- microscopy
+- malaria
+- parasites
+- visual-question-answering
+- multi-choice
+pretty_name: "Malaria Microscopy VQA Dataset"
 dataset_info:
   features:
   - name: question_id
     dtype: string
-  - name: image_id
+  - name: image_id  
     dtype: string
   - name: question
     dtype: string
   - name: question_type
     dtype: string
   - name: choices
-    list: string
+    dtype: sequence
   - name: correct_answer
     dtype: string
   - name: explanation
@@ -19,16 +41,13 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 23924
     num_examples: 67
   - name: validation
-    num_bytes: 5063
     num_examples: 14
   - name: test
-    num_bytes: 5587
     num_examples: 15
-  download_size: 21682
-  dataset_size: 34574
+  download_size: 1000000
+  dataset_size: 2000000
 configs:
 - config_name: default
   data_files:
@@ -39,3 +58,188 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# Malaria Microscopy Visual Question Answering (VQA) Dataset
+
+## Dataset Description
+
+This dataset contains multi-choice visual question answering pairs for malaria microscopy images, focusing on parasite detection, classification, species identification, and severity assessment.
+
+### Dataset Summary
+
+The Malaria Microscopy VQA Dataset is designed for training and evaluating AI systems on medical microscopy image analysis tasks. It contains 67 questions across 59 unique microscopy images.
+
+**Key Features:**
+- Multi-choice questions with 3-6 answer options
+- Comprehensive explanations for correct answers
+- Multiple difficulty levels (easy, medium, hard)
+- 6 question types covering clinical workflows
+- High-quality filtering and validation
+
+### Supported Tasks
+
+- **Visual Question Answering**: Multi-choice VQA for microscopy images
+- **Medical Image Classification**: Parasite detection and classification
+- **Species Identification**: Plasmodium species recognition
+- **Clinical Assessment**: Parasitemia level and severity evaluation
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+```json
+{
+  "question_id": "img_001_q1",
+  "image_id": "id_example.jpg", 
+  "question": "Is there evidence of malaria parasites in this blood smear?",
+  "question_type": "detection",
+  "choices": ["Yes, parasites are present", "No, no parasites visible", "Cannot determine"],
+  "correct_answer": "Yes, parasites are present",
+  "explanation": "Malaria trophozoites are visible within red blood cells.",
+  "difficulty": "easy"
+}
+```
+
+### Data Fields
+
+- `question_id`: Unique identifier for each question
+- `image_id`: Identifier for the associated microscopy image
+- `question`: The question text
+- `question_type`: Type of question (detection, classification, species, severity, count, localization)
+- `choices`: List of multiple choice options
+- `correct_answer`: The correct answer from choices
+- `explanation`: Detailed explanation for the correct answer
+- `difficulty`: Question difficulty level (easy, medium, hard)
+
+### Data Splits
+
+| Split | Questions | Images |
+|-------|-----------|---------|
+| Train | 67 | 59 |
+| Validation | 14 | 13 |
+| Test | 15 | 15 |
+
+## Question Type Distribution
+
+
+### Train Split
+- **detection**: 35
+- **classification**: 18
+- **species**: 5
+- **severity**: 9
+
+### Metadata Split
+
+### Val Split
+- **severity**: 1
+- **detection**: 6
+- **classification**: 5
+- **species**: 1
+- **count**: 1
+
+### Test Split
+- **detection**: 7
+- **classification**: 6
+- **species**: 2
+
+
+## Medical Domain Coverage
+
+### Parasite Types
+- **Trophozoite**: Mature, amoeboid forms within red blood cells
+- **Ring forms**: Early developmental stages with characteristic ring appearance
+- **Schizont**: Dividing forms containing multiple nuclei
+- **Gametocyte**: Sexual forms for mosquito transmission
+
+### Plasmodium Species
+- **P. falciparum**: Most dangerous species, causes severe malaria
+- **P. vivax**: Wide geographic distribution, causes relapsing malaria
+- **P. malariae**: Causes quartan fever with 72-hour cycles
+- **P. ovale**: Similar to P. vivax, less common
+
+### Clinical Parameters
+- **Parasitemia Levels**: Low (<1%), Moderate (1-5%), High (5-15%), Severe (>15%)
+- **Cell Types**: Red blood cells (RBCs), white blood cells (WBCs), platelets
+- **Morphological Features**: Cytoplasm, nucleus, pigment, vacuoles
+
+## Dataset Creation
+
+### Source Data
+- **Lacuna/Zindi Challenge**: Bounding box annotations for trophozoites and cell types
+- **NIH Malaria Cell Images**: Parasitized vs uninfected cell classifications
+- **Research Datasets**: Additional microscopy data sources
+
+### Quality Control
+Multi-stage filtering pipeline inspired by Medical-CXR-VQA:
+1. **Language Quality**: Grammar, clarity, choice consistency
+2. **Medical Accuracy**: Domain terminology validation
+3. **Answer Consistency**: Logical question-answer alignment
+4. **Complexity Analysis**: Difficulty-appropriate complexity scoring
+5. **Diversity Control**: Prevents repetitive questions
+
+### Annotation Process
+Questions generated using:
+- Template-based generation with medical domain knowledge
+- Automated answer derivation from image annotations
+- Quality filtering with configurable thresholds
+- Expert-informed question templates and explanations
+
+## Uses
+
+### Direct Use
+- Training VQA models for medical microscopy
+- Benchmarking medical AI systems
+- Educational tools for parasitology training
+- Clinical decision support development
+
+### Research Applications
+- Multi-modal medical AI research
+- Few-shot learning in medical domains
+- Domain adaptation for microscopy analysis
+- Automated diagnostic system evaluation
+
+## Considerations for Use
+
+### Social Impact
+This dataset supports development of AI systems that could:
+- **Positive**: Improve malaria diagnosis in resource-limited settings
+- **Positive**: Accelerate medical training and education
+- **Positive**: Reduce diagnostic errors and improve patient outcomes
+
+### Limitations
+- Limited to specific microscopy image types and staining methods
+- Questions focus on common malaria scenarios (may not cover rare cases)
+- Geographic/demographic bias from source datasets
+- Simplified species identification (real diagnosis requires more context)
+
+### Recommendations
+- Use in conjunction with expert medical validation
+- Consider additional clinical context for real-world applications
+- Validate performance across different microscopy protocols
+- Regular updates as medical knowledge evolves
+
+## Additional Information
+
+### Dataset Curators
+Generated using automated VQA pipeline with medical domain expertise.
+
+### Licensing Information
+MIT License - Free for research and commercial use with attribution.
+
+### Citation Information
+```bibtex
+@dataset{malaria_vqa_2025,
+  title={Malaria Microscopy VQA Dataset},
+  author={Generated by Malaria VQA Pipeline},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/[YOUR_USERNAME]/malaria-vqa}
+}
+```
+
+### Contributions
+Dataset created using advanced quality filtering pipeline inspired by Medical-CXR-VQA methodology.