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.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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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.
Extends online 2D multi-camera tracking to 3D via depth-based point cloud reconstruction, clustering for 3D boxes, and local ID consistency for global data association, placing 3rd on 2025 AI City Challenge 3D MTMC dataset.
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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.