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Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning
April 12, 2024, 4:42 a.m. | Pranav Kulkarni, Adway Kanhere, Harshita Kukreja, Vivian Zhang, Paul H. Yi, Vishwa S. Parekh
cs.LG updates on arXiv.org arxiv.org
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.
abstract acquisition adversarial arxiv center cs.cv cs.lg data eess.iv external data federated learning gan gans generative generative adversarial network however improving network proton show synthesis type
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