mlsanityData to 14 Oct 2026

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

arXiv 2601.19380 ↗First posted 27 Jan 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 27 Jan 2026.

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References · 9

  1. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 613
  2. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  3. 2019Med3D: Transfer Learning for 3D Medical Image Analysiscited by 288
  4. 2016Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challengecited by 201
  5. 2024VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imagingcited by 64
  6. 2021nnDetection: A Self-configuring Method for Medical Object Detectioncited by 60
  7. 2024Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detectioncited by 6
  8. 2024Reproducible Benchmarking for Lung Nodule Detection and Malignancy Classification Across Multiple Low-Dose CT Datasetscited by 4
  9. 2025Virtual Patients, Real Gains: Digital Twin-Based Simulated CT for Multitask Lung Nodule Analysiscited by 1

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