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Updated: Sep 17, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.

Cheng-Shun Lee1,2, Jenn-Jier James Lien1, Kai Chain3

  • 1Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 701401, Taiwan.

BMC Medical Imaging
|July 2, 2025
PubMed
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This study introduces a novel federated learning approach with a teacher-student framework and semisupervised learning for improved computed tomography (CT) liver tumor detection. The method enhances accuracy while reducing the need for extensive expert annotations.

Area of Science:

  • Medical Imaging Analysis
  • Machine Learning in Healthcare
  • Radiology AI

Background:

  • Computed tomography (CT) scan analysis for liver tumors is crucial but labor-intensive.
  • Training effective machine learning models requires extensive expert annotations.
  • Current methods face challenges in efficiency and data requirements.

Purpose of the Study:

  • To develop an innovative approach for improving CT-based liver tumor detection.
  • To significantly reduce the labor and time costs associated with training detection models.
  • To enhance model accuracy while minimizing reliance on large annotated datasets.

Main Methods:

  • Utilized federated learning for privacy-preserving collaborative model training across institutions.
  • Implemented a teacher-student framework with an enhanced slice-aware network (SANet).
Keywords:
Federated learningLiver tumorsMedical image analysisSemisupervised learningTeacher–student framework

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  • Employed semisupervised learning (SSL) techniques to leverage pseudolabels from a teacher model.

    • Main Results:

    • The proposed method achieved a model accuracy of 83% in simulation experiments.
    • Demonstrated improvement over original locally trained models.
    • Successfully reduced dependence on extensively annotated datasets.

    Conclusions:

    • The combined approach of federated learning, teacher-student SANet, and SSL offers a promising solution for CT liver tumor detection.
    • The method enhances detection accuracy while substantially lowering labor and time costs.
    • Achieved 83% model accuracy, representing a significant advancement over previous techniques.