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arXiv Open Access 2024

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

Pranav Kulkarni Adway Kanhere Harshita Kukreja Vivian Zhang Paul H. Yi +1 lainnya
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Abstrak

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.

Topik & Kata Kunci

eess.IV cs.CV cs.LG

Penulis (6)

P

Pranav Kulkarni

A

Adway Kanhere

H

Harshita Kukreja

V

Vivian Zhang

P

Paul H. Yi

V

Vishwa S. Parekh

Format Sitasi

APA MLA BibTeX
Kulkarni, P., Kanhere, A., Kukreja, H., Zhang, V., Yi, P.H., Parekh, V.S. (2024). Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning. https://arxiv.org/abs/2404.07374

Akses Cepat

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Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Sumber Database
arXiv
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Open Access ✓