One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training

• URL: http://arxiv.org/abs/2512.24278v1
• Date: Tue, 30 Dec 2025 15:07:09 GMT
• Title: One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training
• Authors: Jia Yu, Yan Zhu, Peiyao Fu, Tianyi Chen, Zhihua Wang, Fei Wu, Quanlin Li, Pinghong Zhou, Shuo Wang, Xian Yang,
• Abstract summary: EndoRare is a one-shot, retraining-free generative framework that synthesizes diverse, high-fidelity lesion exemplars from a single reference image.<n>We validated the framework across four rare pathologies.<n>These results establish a practical, data-efficient pathway to bridge the rare-disease gap in both computer-aided diagnostics and clinical education.
• Score: 45.49415063761575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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