Specificity-Preserving Federated Learning for MR Image Reconstruction
- URL: http://arxiv.org/abs/2112.05752v1
- Date: Thu, 9 Dec 2021 22:13:35 GMT
- Title: Specificity-Preserving Federated Learning for MR Image Reconstruction
- Authors: Chun-Mei Feng and Yunlu Yan and Huazhu Fu and Yong Xu and Ling Shao
- Abstract summary: Federated learning can be used to improve data privacy and efficiency in magnetic resonance (MR) image reconstruction.
Recent FL techniques tend to solve this by enhancing the generalization of the global model.
We propose a specificity-preserving FL algorithm for MR image reconstruction (FedMRI)
- Score: 94.58912814426122
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Federated learning (FL) can be used to improve data privacy and efficiency in
magnetic resonance (MR) image reconstruction by enabling multiple institutions
to collaborate without needing to aggregate local data. However, the domain
shift caused by different MR imaging protocols can substantially degrade the
performance of FL models. Recent FL techniques tend to solve this by enhancing
the generalization of the global model, but they ignore the domain-specific
features, which may contain important information about the device properties
and be useful for local reconstruction. In this paper, we propose a
specificity-preserving FL algorithm for MR image reconstruction (FedMRI). The
core idea is to divide the MR reconstruction model into two parts: a globally
shared encoder to obtain a generalized representation at the global level, and
a client-specific decoder to preserve the domain-specific properties of each
client, which is important for collaborative reconstruction when the clients
have unique distribution. Moreover, to further boost the convergence of the
globally shared encoder when a domain shift is present, a weighted contrastive
regularization is introduced to directly correct any deviation between the
client and server during optimization. Extensive experiments demonstrate that
our FedMRI's reconstructed results are the closest to the ground-truth for
multi-institutional data, and that it outperforms state-of-the-art FL methods.
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