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.

翻译：利用在公共数据集上训练的多个开放获取模型，我们开发了Tri-Reader——一个全面、可免费获取的流程。该流程将肺部分割、结节检测和恶性程度分类集成到一个统一的三阶段工作流中。该流程旨在优先保证敏感性，同时减轻标注人员的候选负担。为确保在不同实践中的准确性和泛化能力，我们在多个内部和外部数据集上评估了Tri-Reader，并与专家标注和数据集提供的参考标准进行了比较。