Learning Federated Visual Prompt in Null Space for MRI Reconstruction
- URL: http://arxiv.org/abs/2303.16181v2
- Date: Thu, 30 Mar 2023 08:00:35 GMT
- Title: Learning Federated Visual Prompt in Null Space for MRI Reconstruction
- Authors: Chun-Mei Feng, Bangjun Li, Xinxing Xu, Yong Liu, Huazhu Fu, Wangmeng
Zuo
- Abstract summary: We propose a new algorithm, FedPR, to learn federated visual prompts in the null space of global prompt for MRI reconstruction.
FedPR significantly outperforms state-of-the-art FL algorithms with 6% of communication costs when given the limited amount of local training data.
- Score: 83.71117888610547
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple
hospitals to collaborate distributedly without aggregating local data, thereby
protecting patient privacy. However, the data heterogeneity caused by different
MRI protocols, insufficient local training data, and limited communication
bandwidth inevitably impair global model convergence and updating. In this
paper, we propose a new algorithm, FedPR, to learn federated visual prompts in
the null space of global prompt for MRI reconstruction. FedPR is a new
federated paradigm that adopts a powerful pre-trained model while only learning
and communicating the prompts with few learnable parameters, thereby
significantly reducing communication costs and achieving competitive
performance on limited local data. Moreover, to deal with catastrophic
forgetting caused by data heterogeneity, FedPR also updates efficient federated
visual prompts that project the local prompts into an approximate null space of
the global prompt, thereby suppressing the interference of gradients on the
server performance. Extensive experiments on federated MRI show that FedPR
significantly outperforms state-of-the-art FL algorithms with <6% of
communication costs when given the limited amount of local training data.
Related papers
- Zero-shot Dynamic MRI Reconstruction with Global-to-local Diffusion Model [17.375064910924717]
We propose a dynamic MRI reconstruction method based on a time-interleaved acquisition scheme, termed the Glob-al-to-local Diffusion Model.
The proposed method performs well in terms of noise reduction and preservation, achieving reconstruction quality comparable to that of supervised approaches.
arXiv Detail & Related papers (2024-11-06T07:40:27Z) - Accelerated Multi-Contrast MRI Reconstruction via Frequency and Spatial Mutual Learning [50.74383395813782]
We propose a novel Frequency and Spatial Mutual Learning Network (FSMNet) to explore global dependencies across different modalities.
The proposed FSMNet achieves state-of-the-art performance for the Multi-Contrast MR Reconstruction task with different acceleration factors.
arXiv Detail & Related papers (2024-09-21T12:02:47Z) - FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging [12.307490659840845]
We introduce FedMRL, a novel multi-agent deep reinforcement learning framework designed to address data heterogeneity.
FedMRL incorporates a novel loss function to facilitate fairness among clients, preventing bias in the final global model.
We assess our approach using two publicly available real-world medical datasets, and the results demonstrate that FedMRL significantly outperforms state-of-the-art techniques.
arXiv Detail & Related papers (2024-07-08T10:10:07Z) - Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping [64.58402571292723]
We propose a manifold reshaping approach called FedMR to calibrate the feature space of local training.
We conduct extensive experiments on a range of datasets to demonstrate that our FedMR achieves much higher accuracy and better communication efficiency.
arXiv Detail & Related papers (2024-05-29T10:56:13Z) - Learning Personalized Brain Functional Connectivity of MDD Patients from
Multiple Sites via Federated Bayesian Networks [9.873532358701803]
We propose a federated joint estimator, NOTEARS-PFL, for simultaneous learning of multiple Bayesian networks.
We evaluate the performance of the proposed method on both synthetic and real-world multi-site rs-fMRI datasets.
arXiv Detail & Related papers (2023-01-06T08:58:06Z) - Specificity-Preserving Federated Learning for MR Image Reconstruction [94.58912814426122]
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)
arXiv Detail & Related papers (2021-12-09T22:13:35Z) - Complex-valued Federated Learning with Differential Privacy and MRI Applications [51.34714485616763]
We introduce the complex-valued Gaussian mechanism, whose behaviour we characterise in terms of $f$-DP, $(varepsilon, delta)$-DP and R'enyi-DP.
We present novel complex-valued neural network primitives compatible with DP.
Experimentally, we showcase a proof-of-concept by training federated complex-valued neural networks with DP on a real-world task.
arXiv Detail & Related papers (2021-10-07T14:03:00Z) - Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial
Transformers [0.0]
We introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adrial TransformERs (SLATER)
A zero-shot reconstruction is performed on undersampled test data, where inference is performed by optimizing network parameters.
Experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against several state-of-the-art unsupervised methods.
arXiv Detail & Related papers (2021-05-15T02:01:21Z) - Multi-institutional Collaborations for Improving Deep Learning-based
Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
Deep learning methods have been shown to produce superior performance on MR image reconstruction.
These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations.
We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
arXiv Detail & Related papers (2021-03-03T03:04:40Z) - Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and
Domain Adaptation: ABIDE Results [13.615292855384729]
To train a high-quality deep learning model, the aggregation of a significant amount of patient information is required.
Due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions.
Federated learning allows for population-level models to be trained without centralizing entities' data.
arXiv Detail & Related papers (2020-01-16T04:49:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.