14 citations · 45 across the 8 of their papers we have counts for
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eess.IV2023
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…
cs.LG2023★ 6 cited
Rethinking Client Drift in Federated Learning: A Logit Perspective
Yunlu Yan, Chun-Mei Feng, Mang Ye +5
Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. However, the real-world non-IID data will lead to c…
eess.IV2023★ 2 cited
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…