14 citations · 45 across the 8 of their papers we have counts for
6 papers · 1 filter
Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction
Yunlu Yan, Chun-Mei Feng, Yuexiang Li +2
While multi-modal learning has been widely used for MRI reconstruction, it relies on paired multi-modal data which is difficult to acquire in real clinical scenarios. Especially in…
Cross-Modal Vertical Federated Learning for MRI Reconstruction
Yunlu Yan, Hong Wang, Yawen Huang +5
Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from d…
Specificity-Preserving Federated Learning for MR Image Reconstruction
Chun-Mei Feng, Yunlu Yan, Shanshan Wang +3
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…
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
Chun-Mei Feng, Yunlu Yan, Kai Yu +3
Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information,…
Multi-Modal Transformer for Accelerated MR Imaging
Chun-Mei Feng, Yunlu Yan, Geng Chen +3
Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from it…
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
Chun-Mei Feng, Yunlu Yan, Huazhu Fu +2
The core problem of Magnetic Resonance Imaging (MRI) is the trade off between acceleration and image quality. Image reconstruction and super-resolution are two crucial techniques i…