S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images
This work addresses dataset limitations for AI in dermatology, though it is incremental as it builds on existing synthetic data methods for medical imaging.
The authors tackled the challenge of limited and biased datasets in dermatology AI by developing S-SYNTH, a knowledge-based framework for generating synthetic skin images with controlled variations, showing that results from synthetic data align with trends from real images while addressing issues like small size and lack of diversity.
Development of artificial intelligence (AI) techniques in medical imaging requires access to large-scale and diverse datasets for training and evaluation. In dermatology, obtaining such datasets remains challenging due to significant variations in patient populations, illumination conditions, and acquisition system characteristics. In this work, we propose S-SYNTH, the first knowledge-based, adaptable open-source skin simulation framework to rapidly generate synthetic skin, 3D models and digitally rendered images, using an anatomically inspired multi-layer, multi-component skin and growing lesion model. The skin model allows for controlled variation in skin appearance, such as skin color, presence of hair, lesion shape, and blood fraction among other parameters. We use this framework to study the effect of possible variations on the development and evaluation of AI models for skin lesion segmentation, and show that results obtained using synthetic data follow similar comparative trends as real dermatologic images, while mitigating biases and limitations from existing datasets including small dataset size, lack of diversity, and underrepresentation.