Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning
<|reference_start|>Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning: 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.<|reference_end|>
@article{kulkarni2024improving,
    title={Improving Multi-Center Generalizability of GAN-Based Fat Suppression
  using Federated Learning},
    author={Pranav Kulkarni, Adway Kanhere, Harshita Kukreja, Vivian Zhang, Paul
  H. Yi, Vishwa S. Parekh},
    journal={arXiv preprint arXiv:2404.07374},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07374},
    primaryClass={eess.IV cs.CV cs.LG}
}