Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning
This work addresses the problem of limited generalizability in medical imaging for clinicians and researchers, though it is incremental as it applies an existing method (federated learning) to a specific domain.
The authors tackled the poor generalizability of GANs trained on single-site data for synthesizing fat-suppressed MRIs by using federated learning, resulting in improved multi-center performance while enabling privacy-preserving collaborations.
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.