arXiv：cs.AI· S\'el\'ena Jean-Mactoux, Sophie Loizillon, Ghina Jardali, Amandine Cromb\'e, Nathalie Lassau, Alexandre B\^one·· 16 小时前
CAPTOR：面向胸腹盆肿瘤 CT 的视觉-语言基础模型
CAPTOR: a vision-language foundation model for Chest-Abdomen-Pelvis Oncologic CT
AI 导读
研究团队开发了视觉-语言基础模型 CAPTOR，用于肿瘤胸腹盆（CAP）CT 解读，训练与测试分别使用 25,512 和 10,794 对 CT-报告数据。在分类任务上，CAPTOR 的 CAP 子集 AUC 达 0.767，优于次优模型的 0.695；肿瘤子集 AUC 为 0.784，优于 0.734，并在四个肿瘤预后基准中三项排名第一。该模型的预处理、训练与推理代码及模型权重均已开源。
正文
Abstract:Purpose : To develop and evaluate CAPTOR, a deep-learning vision-language foundation model for oncologic chest-abdomen-pelvis (CAP) CT interpretation.
Materials & Methods : CAPTOR (Chest-Abdomen-Pelvis foundaTion model for Oncologic Radiology) is a contrastive vision-language model, trained using a pretraining strategy for oncologic CAP CT (in single- or multiple-series acquisitions), on a multicenter retrospective dataset from North America, Europe and Asia, predominantly from tumor lesion examinations. CAPTOR was compared with state-of-the-art CT foundation models on three downstream tasks: disease detection, survival prediction using TumorImagingBench and image retrieval. Classification and retrieval were both evaluated on held-out internal and public test sets, stratified by anatomical coverage and indication. Bootstrap resampling was used to estimate confidence intervals, and pairwise comparisons were performed using permutation tests.
Results : CAPTOR was trained and tested on 25,512 and 10,794 CT-report pairs respectively. TumorImagingBench datasets comprised between 134 and 421 studies each. CAPTOR outperformed all baseline models, on the classification task, on the CAP subset (AUC 0.767, 95%CI: 0.734-0.793, vs. 0.695, 95%CI: 0.645-0.736 for the next-best, p<0.001) and oncologic subset (AUC 0.784, 95%CI: 0.759-0.805, vs. 0.734, 95%CI: 0.703-0.767 p<0.001), ranked first on three of the four oncologic prognostic benchmarks, and achieved superior retrieval performance on CAP and oncologic subsets.
Conclusion : A foundation model trained on oncology-enriched CAP CT data outperformed relevant state-of-the-art models in its target clinical context, underscoring the importance of clinically aligned pretraining.
We release our preprocessing, training and inference code : this https URL and model weights : this https URL
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14226 [cs.CV] |
| (or arXiv:2610.14226v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14226 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sophie Loizillon Dr [view email]
[v1]
Fri, 9 Oct 2026 18:39:01 UTC (4,108 KB)
来源：arXiv：cs.AI · arxiv.org