From code sharing to sharing of implementations: Advancing reproducible AI development for medical imaging through federated testing.

Journal: Journal of medical imaging and radiation sciences
Published Date: Aug 29, 2024

Abstract

BACKGROUND: The reproducibility crisis in AI research remains a significant concern. While code sharing has been acknowledged as a step toward addressing this issue, our focus extends beyond this paradigm. In this work, we explore "federated testing" as an avenue for advancing reproducible AI research and development especially in medical imaging. Unlike federated learning, where a model is developed and refined on data from different centers, federated testing involves models developed by one team being deployed and evaluated by others, addressing reproducibility across various implementations.

Authors

  • Fereshteh Yousefirizi
    Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada. Electronic address: frizi@bccrc.ca.
  • Annudesh Liyanage
    Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada.
  • Ivan S Klyuzhin
    Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
  • Arman Rahmim

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