Task:
The client needed labeled images to train and validate a computer vision model that classifies female pattern hair loss. Each participant provided two photos (a top view and a frontal view) showing the scalp, hairline, and forehead area.
The dataset was annotated using the Sinclair Scale, which includes five stages of female alopecia. The main target was to collect balanced data for Stages 2, 3, and 4, with 50 validated samples per stage.
Key challenges included:
- Lower participation rates compared to standard biometric tasks
- Difficulty separating borderline cases between Sinclair stages
- Budget limits
- Privacy concerns when collecting identifiable facial data
The Solution
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- 01
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Preparation and guidelines
- Selected the Sinclair Scale as a clinically relevant and easy-to-understand framework for female alopecia assessment
- Built visual-first guidelines using real examples for each stage rather than abstract descriptions
- Added explanations for common participant mistakes, especially confusion between adjacent stages
- Included borderline and transitional cases to clarify where one stage ends and another begins
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- 02
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Data collection process
- Introduced eye-area blurring to reduce biometric sensitivity
- Optimized task pricing to balance budget constraints and sustainable collection speed
- Focused collection on Sinclair Stages 2, 3, and 4, while continuing to review all incoming submissions
- Identified and collected high-quality samples from Stages 1 and 5
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- 03
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Annotation and quality control
- Reviewed all submissions through an experienced assessor with prior alopecia annotation background
- Carefully filtered and reclassified ambiguous cases based on expert judgment
- Delivered only high-confidence, clearly validated samples in the final dataset
Results:
Collected and annotated 150+ female alopecia cases
Met the required distribution across Sinclair Stages 2, 3, and 4
Expanded the dataset with validated Stage 1 and Stage 5 examples
Delivered a clean, well-structured dataset ready for medical AI training
