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Found 8 papers, 4 papers with code
Rethinking Client Drift in Federated Learning: A Logit Perspective
Concretely, FedCSD introduces a class prototype similarity distillation to align the local logits with the refined global logits that are weighted by the similarity between local logits and the global prototype.
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction
In this paper, we propose a novel communication-efficient federated learning framework, namely Fed-PMG, to address the missing modality challenge in federated multi-modal MRI reconstruction.
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
To achieve this goal, we propose FedRDN, a simple yet remarkably effective data augmentation method for feature distribution skewed FL, which randomly injects the statistics of the dataset from the entire federation into the client's data.
Cross-Modal Vertical Federated Learning for MRI Reconstruction
To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which shape data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals.
Specificity-Preserving Federated Learning for MR Image Reconstruction
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.
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image.
Multi-Modal Transformer for Accelerated MR Imaging
To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging.
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
Then, a task transformer module is designed to embed and synthesize the relevance between the two tasks.
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