README.md

---
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
    dtype: image
  - name: question
    dtype: string
  - name: question_type
    dtype: string
  - name: choices
    list: string
  - name: correct_answer
    dtype: string
  - name: explanation
    dtype: string
  - name: difficulty
    dtype: string
  splits:
  - name: train
    num_bytes: 17427191758.492
    num_examples: 23382
  - name: validation
    num_bytes: 2194634787.174
    num_examples: 2922
  - name: test
    num_bytes: 2237957103.036
    num_examples: 2924
  download_size: 21234958321
  dataset_size: 21859783648.702
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - 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.