A new benchmark dataset of 456 real rare-disease face images demonstrates that phenotype-aware synthetic augmentation with landmark filtering improves AI diagnostic accuracy by up to 13.7% in ultra-low-data regimes.
Why rare diseases are an important medical and so- cial issue.The Lancet, 371(9629):2039–2041
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Synthetic facial images alone can train models for pediatric rare disease recognition to performance levels comparable to real-data baselines when generated at sufficient scale.
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RDFace: A Benchmark Dataset for Rare Disease Facial Image Analysis under Extreme Data Scarcity and Phenotype-Aware Synthetic Generation
A new benchmark dataset of 456 real rare-disease face images demonstrates that phenotype-aware synthetic augmentation with landmark filtering improves AI diagnostic accuracy by up to 13.7% in ultra-low-data regimes.
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Synthetic Data Alone is Enough? Rethinking Data Scarcity in Pediatric Rare Disease Recognition
Synthetic facial images alone can train models for pediatric rare disease recognition to performance levels comparable to real-data baselines when generated at sufficient scale.