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.
Curran Associates Inc., Red Hook, NY , USA
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
other 1
citation-polarity summary
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.