RDFace：极端数据稀缺下罕见病面部图像分析的基准数据集与表型感知合成生成
RDFace: A Benchmark Dataset for Rare Disease Facial Image Analysis under Extreme Data Scarcity and Phenotype-Aware Synthetic Generation
在极端低数据条件下（每类平均 4.4 张）做细粒度面部表型分类时，可参考其用 DreamBooth/FastGAN 合成样本、再用面部关键点相似度过滤以保持表型保真度的流程，以及用视觉语言模型生成表型描述并计算报告相似度来校验合成图像语义有效性的评估思路。
原文依据 Generated images are filtered via facial landmark similarity to maintain phenotype fidelity and merged with real data
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论罕见病常在儿童身上表现出独特的面部表型，为临床医生和 AI 辅助筛查系统提供了有价值的诊断线索。然而，该领域的进展受到严重制约：一方面缺乏经过整理、来源合乎伦理的面部数据，另一方面不同病症之间的表型高度相似。为应对这些挑战，我们提出 RDFace，一个经过整理的基准数据集，包含 456 张儿童面部图像，覆盖 103 种罕见遗传病（每种病症平均 4.4 个样本）。每张经伦理核验的图像都配有标准化元数据。RDFace 支持在真实世界的低数据约束下开发与评估数据高效的罕见病诊断 AI 模型。我们通过交叉验证对多个预训练视觉骨干网络进行了基准测试，并探索了基于 DreamBooth 与 FastGAN 的合成数据增强。生成的图像通过面部关键点相似度进行过滤，以保持表型保真度，并与真实数据合并，在极端低数据情形下将诊断准确率最多提升 13.7%。为评估语义有效性，由视觉语言模型从真实图像与合成图像生成的表型描述取得了 0.84 的报告相似度得分。RDFace 为公平的罕见病 AI 研究建立了一个透明、可直接用于基准评测的数据集，并提供了一个可扩展的框架，用于同时评估诊断性能与合成医学图像的完整性。
英文原文摘要 1452 字符
Rare diseases often manifest with distinctive facial phenotypes in children, offering valuable diagnostic cues for clinicians and AI-assisted screening systems. However, progress in this field is severely limited by the scarcity of curated, ethically sourced facial data and the high similarity among phenotypes across different conditions. To address these challenges, we introduce RDFace, a curated benchmark dataset comprising 456 pediatric facial images spanning 103 rare genetic conditions (average 4.4 samples per condition). Each ethically verified image is paired with standardized metadata. RDFace enables the development and evaluation of data-efficient AI models for rare disease diagnosis under real-world low-data constraints. We benchmark multiple pretrained vision backbones using cross-validation and explore synthetic augmentation with DreamBooth and FastGAN. Generated images are filtered via facial landmark similarity to maintain phenotype fidelity and merged with real data, improving diagnostic accuracy by up to 13.7% in ultra-low-data regimes. To assess semantic validity, phenotype descriptions generated by a vision-language model from real and synthetic images achieve a report similarity score of 0.84. RDFace establishes a transparent, benchmark-ready dataset for equitable rare disease AI research and presents a scalable framework for evaluating both diagnostic performance and the integrity of synthetic medical imagery.