Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Federated learning, multi-institutional, collaborative learning, image reconstruction
Motivation: Federated learning (FL) enables privacy-preserving training of deep reconstruction models across multiple sites to improve generalization at the expense of lower within-site performance. Yet, existing methods require a common model architecture across sites, limiting flexibility.
Goal(s): Our goal was to devise an architecture-agnostic method for collaborative training of heterogeneous models across sites.
Approach: We introduced a novel peer-to-peer generative learning method (PGL-FedMR), where individual sites share a generative prior for their MRI data with remaining sites, and prior-driven synthetic data are used to train reconstruction models at each site.
Results: PGL-FedMR improves across-site generalization over local models, and within-site performance over conventional FL.
Impact: Improvements in within-site and across-site performance for MRI reconstruction through PGL-FedMR, coupled with the ability to handle heterogeneous architectures, may facilitate privacy-preserving multi-institutional collaborations to build reliable reconstruction models for many applications where data are scarce including rare diseases.
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