Datasets:

EricPeter
/

malaria-microscopy-vqa

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

{
  "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:

Language Quality: Grammar, clarity, choice consistency
Medical Accuracy: Domain terminology validation
Answer Consistency: Logical question-answer alignment
Complexity Analysis: Difficulty-appropriate complexity scoring
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

@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.