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Related Experiment Video

Updated: May 28, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

Hybrid Curriculum Learning for Data-Efficient Lung Nodule Detection with YOLOv11.

Yi Luo1, Yike Guo1, Hamed Hooshangnejad2

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Diagnostics (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

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This study introduces a hybrid curriculum learning framework to improve lung nodule detection in chest CT scans, enhancing accuracy and efficiency in cancer screening.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Accurate pulmonary nodule detection on chest CT is vital for lung cancer screening.
  • High annotation costs hinder the development of robust nodule detectors.
  • This study addresses data efficiency in training these detectors.

Purpose of the Study:

  • To systematically study curriculum learning for CT-based lung nodule detection.
  • To propose a hybrid curriculum learning framework for data-efficient optimization.
  • To evaluate the framework on the enhanced LUNA25 benchmark.

Main Methods:

  • A hybrid curriculum learning framework was developed, fusing handcrafted features (nodule size, count) and model-driven signals (prediction confidence).
  • A three-stage curriculum progressed from easy to hard samples.
Keywords:
YOLOchest CTcurriculum learningdata scarcitylung nodule detection

Related Experiment Videos

Last Updated: May 28, 2026

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

  • The approach used YOLOv11s as a baseline and compared it to conventional training and single-source curricula.

    • Main Results:

    • Hybrid curriculum learning improved mAP50 (0.672 to 0.696) and mAP50-95 (0.369 to 0.385) on the LUNA25 test set.
    • Recall increased from 0.588 to 0.634, and precision from 0.725 to 0.764.
    • Consistent gains were observed in data-efficiency experiments with reduced datasets.

    Conclusions:

    • Combining image complexity and optimization feedback offers effective sample scheduling for lung nodule detection.
    • The proposed hybrid curriculum learning framework enhances robustness and data efficiency.
    • This approach is promising for optimizing lung cancer screening tools.