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Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning

arXiv 2404.07374 ↗First posted 10 Apr 2024eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

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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References · 7

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  2. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  3. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 131
  4. 2016Medical Image Synthesis with Context-Aware Generative Adversarial Networkscited by 91
  5. 2020FedGAN: Federated Generative Adversarial Networks for Distributed Datacited by 89
  6. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  7. 2022FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesiscited by 21

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