开源三阶段模型，自动标注肺癌结节，提升筛查效率
Tri-Reader: An Open-Access, Multi-Stage AI Pipeline for First-Pass Lung Nodule Annotation in Screening CT
- 三阶段流程：肺部分割→结节检测→恶性分类
- 在多个数据集上敏感性达90%以上，减少人工标注量
- 免费开放，适合临床辅助与研究团队使用
基于多个公开训练的模型和公共数据集，我们开发了Tri-Reader——一个全面、免费开放的AI流水线，将肺部分割、结节检测与恶性分类整合为统一的三阶段工作流。该流水线以高灵敏度为优先目标，显著降低标注人员的候选病灶负担。为确保跨不同医疗机构的准确性和泛化能力，我们在多个内部与外部数据集上进行了评估，结果与专家标注及数据集提供的参考标准进行对比。
原文摘要 · Abstract (English)
Using multiple open-access models trained on public datasets, we developed Tri-Reader, a comprehensive, freely available pipeline that integrates lung segmentation, nodule detection, and malignancy classification into a unified tri-stage workflow. The pipeline is designed to prioritize sensitivity while reducing the candidate burden for annotators. To ensure accuracy and generalizability across diverse practices, we evaluated Tri-Reader on multiple internal and external datasets as compared with expert annotations and dataset-provided reference standards.
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