Tri-Reader: An Open-Access, Multi-Stage AI Pipeline for First-Pass Lung Nodule Annotation in Screening CT

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Authors: Fakrul Islam Tushar, Joseph Yuan-Chieh Lo
Cited by: 0

Abstract

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.

Fields of Study & Subjects

AI in cancer detection Lung Cancer Diagnosis and Treatment Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
arXiv (Cornell University)
Publication date
27 January 2026
Document type
Preprint