Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Detecting liver tumors via computed tomography (CT) scans is a critical but labor-intensive task. Extensive expert annotations are needed to train effective machine learning models. This study presents an innovative approach that leverages federated learning in combination with a teacher‒student framework, an enhanced slice-aware network (SANet), and semisupervised learning (SSL) techniques to improve the CT-based liver tumor detection process while significantly reducing its labor and time costs.

Authors

  • Cheng-Shun Lee
    Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 701401, Taiwan.
  • Jenn-Jier James Lien
    Department of Computer Science and Information Engineering, National Cheng Kung University.
  • Kai Chain
    Department of Information Engineering, I-Shou University, Kaohsiung, 84001, Taiwan.
  • Li-Chun Huang
    Office of Medical Education and Research, Zouying Armed Forces General Hospital, Kaohsiung, 813204, Taiwan. turtle520@mail2000.com.tw.
  • Zhong-Wei Hsu
    Foxconn Technology Group, New Taipei, 236040, Taiwan.

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