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Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning
Published 10 Apr 2024 in eess.IV, cs.CV, and cs.LG | (2404.07374v1)
Abstract: Generative Adversarial Network (GAN)-based synthesis of fat suppressed (FS) MRIs from non-FS proton density sequences has the potential to accelerate acquisition of knee MRIs. However, GANs trained on single-site data have poor generalizability to external data. We show that federated learning can improve multi-center generalizability of GANs for synthesizing FS MRIs, while facilitating privacy-preserving multi-institutional collaborations.
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