One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911347081478144 |
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| author | Yu, Jia Zhu, Yan Fu, Peiyao Chen, Tianyi Wang, Zhihua Wu, Fei Li, Quanlin Zhou, Pinghong Wang, Shuo Yang, Xian |
| author_facet | Yu, Jia Zhu, Yan Fu, Peiyao Chen, Tianyi Wang, Zhihua Wu, Fei Li, Quanlin Zhou, Pinghong Wang, Shuo Yang, Xian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24278 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training Yu, Jia Zhu, Yan Fu, Peiyao Chen, Tianyi Wang, Zhihua Wu, Fei Li, Quanlin Zhou, Pinghong Wang, Shuo Yang, Xian Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2512.24278 |