用一张图生成多种罕见胃肠道病变图像，提升诊断与培训效果
One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training
- 仅需一张参考图，通过语言引导分离关键病征与非诊断特征
- 合成图像使新手医生召回率提升0.400，精确率提升0.267
- 适用于罕见病AI训练与临床教学，无需重新训练模型
罕见胃肠道病变在常规内镜中极少出现，限制了人工智能（AI）模型的可靠发展和新手医师的培训。本文提出EndoRare，一种无需重训练的一次性生成框架，可从单张参考图像生成多样且高保真的病变样本。通过语言引导的概念解耦，该框架将典型病征特征与非诊断属性分离，将前者编码为可学习原型嵌入，同时变化后者以保证多样性。我们在四种罕见疾病（钙化纤维瘤、幼年性息肉病综合征、家族性腺瘤性息肉病和佩茨-杰格赫斯综合征）上验证了该框架。专家评估认为合成图像具有临床可信度；用于数据增强后，显著提升了下游AI分类器性能，在低假阳性率下提高真阳性率。关键的是，盲法读者研究显示，接触生成病例的新手内镜医师召回率提升0.400，精确率提升0.267。结果证明，EndoRare为计算机辅助诊断与临床教育中的罕见病差距提供了高效可行的解决方案。
原文摘要 · Abstract (English)
Rare gastrointestinal lesions are infrequently encountered in routine endoscopy, restricting the data available for developing reliable artificial intelligence (AI) models and training novice clinicians. Here we present EndoRare, a one-shot, retraining-free generative framework that synthesizes diverse, high-fidelity lesion exemplars from a single reference image. By leveraging language-guided concept disentanglement, EndoRare separates pathognomonic lesion features from non-diagnostic attributes, encoding the former into a learnable prototype embedding while varying the latter to ensure diversity. We validated the framework across four rare pathologies (calcifying fibrous tumor, juvenile polyposis syndrome, familial adenomatous polyposis, and Peutz-Jeghers syndrome). Synthetic images were judged clinically plausible by experts and, when used for data augmentation, significantly enhanced downstream AI classifiers, improving the true positive rate at low false-positive rates. Crucially, a blinded reader study demonstrated that novice endoscopists exposed to EndoRare-generated cases achieved a 0.400 increase in recall and a 0.267 increase in precision. These results establish a practical, data-efficient pathway to bridge the rare-disease gap in both computer-aided diagnostics and clinical education.
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