.png){kind=icon}
返回
Self-Supervised Federated Learning for Fast MR Imaging
.png){kind=icon}
OA
AI
DOI：10.1109/TIM.2023.3331413.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Federated learning (FL)-based magnetic resonance (MR) image reconstruction can facilitate learning valuable priors from multisite institutions without violating patient's privacy for accelerating MR imaging. However, existing methods rely on fully sampled data for collaborative training of the model. The client that only possesses undersampled data can neither participate in FL nor benefit from other clients. Furthermore, heterogeneous data distributions hinder FL from training an effective deep learning reconstruction model and thus cause performance degradation, and exchanging models frequently causes communication inefficiency. To address these issues, we propose a self-supervised FL method for accelerating MR imaging (SSFedMRI). SSFedMRI explores the physics-based contrastive reconstruction networks in each client to realize cross-site collaborative training in the absence of fully sampled data. Furthermore, a personalized update scheme in the local client is designed to simultaneously capture the global shared representations among different centers and maintain the specific data distribution of each client, and reduce the communication cost by downloading the global model selectively. Extensive experiments demonstrate that SSFedMRI possesses strong capability in reconstructing accurate MR images directly from multiinstitutional undersampled data with low communication cost in FL.
Keyword:
Image reconstruction
Data models
Training
Magnetic resonance imaging
Federated learning
Deep learning
Costs
Deep learning
fast magnetic resonance (MR) imaging
federated learning (FL)
MR imaging (MRI) reconstruction
self-supervised learning
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
分享收藏Distributed contrastive learning for medical image segmentation分布式对比学习在医学图像分割中的应用.png)
MEDICAL IMAGE ANALYSISOAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAI分享收藏
查看更多内容