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Collaborative Screening of COVID-19-like Disease from Multi-Institutional Radiographs: A Federated Learning Approach

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

COVID-19-like pandemics are a major threat to the global health system have the potential to cause high mortality across age groups. The advance of the Internet of Medical Things (IoMT) technologies paves the way toward developing reliable solutions to combat these pandemics. Medical images (i.e., X-rays, computed tomography (CT)) provide an efficient tool for disease detection and diagnosis. The cost, time, and efforts for acquiring and annotating, for instance, large CT datasets make it complicated to obtain large numbers of samples from a single institution. However, owing to the necessity to preserve the privacy of patient data, it is challenging to build a centralized dataset from many institutions, especially during a pandemic. Moreover, heterogeneity between institutions presents a barrier to building efficient screening solutions. Thus, this paper presents a fog-based federated generative domain adaption framework (FGDA), where fog nodes aggregate patients’ data necessary to collaboratively train local deep-learning models for disease screening in medical images from different institutions. Local differential privacy is presented to protect the local gradients against attackers during the global model aggregation. In FGDA, the generative domain adaptation (DA) method is introduced to handle data discrepancies. Experimental evaluation on a case study of COVID-19 segmentation demonstrated the efficiency of FGDA over competing learning approaches with statistical significance.

Original languageEnglish
Article number4766
JournalMathematics
Volume10
Issue number24
DOIs
StatePublished - Dec 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 by the authors.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • COVID-19-like pandemics
  • deep learning
  • domain adaption
  • federated learning
  • internet of medical things

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Mathematics
  • Engineering (miscellaneous)

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