most citedMulti-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities

22 citations · 40 across the 3 of their papers we have counts for

collaborators

3 papers

eess.IV2024★ 1 cited

Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning

Shanshan Wang, Ruoyou Wu, Sen Jia +4

Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the t…

cs.LG2024★ 22 cited

Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities

Kasra Borazjani, Naji Khosravan, Leslie Ying +1

The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When accompanied by the innovative fe…

eess.IV2019★ 17 cited

DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution

Shanshan Wang, Huitao Cheng, Leslie Ying +5

This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Differe…