Multi-institutional PET/CT image segmentation using federated deep transformer learning.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 12, 2023

Abstract

BACKGROUND AND OBJECTIVE: Generalizable and trustworthy deep learning models for PET/CT image segmentation necessitates large diverse multi-institutional datasets. However, legal, ethical, and patient privacy issues challenge sharing of datasets between different centers. To overcome these challenges, we developed a federated learning (FL) framework for multi-institutional PET/CT image segmentation.

Authors

  • Isaac Shiri
    Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Behrooz Razeghi
    Department of Computer Science.
  • Alireza Vafaei Sadr
  • Mehdi Amini
    From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital.
  • Yazdan Salimi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
  • Sohrab Ferdowsi
    HES-SO University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland.
  • Peter Boor
    Institute of Pathology, University Hospital Aachen, RWTH Aachen University, Aachen, Germany.
  • Deniz Gündüz
    Faculty of Engineering, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom.
  • Slava Voloshynovskiy
    Department of Computer Science.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

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