82 citations · 112 across the 10 of their papers we have counts for
10 papers
Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer
Xiangneng Gao, Shulan Ruan, Jun Shi +2
In the treatment of ovarian cancer, precise residual disease prediction is significant for clinical and surgical decision-making. However, traditional methods are either invasive (…
Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction
Qi Wang, Zhijie Wen, Jun Shi +3
Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighte…
Fast MRI Reconstruction via Edge Attention
Hanhui Yang, Juncheng Li, Lok Ming Lui +3
Fast and accurate MRI reconstruction is a key concern in modern clinical practice. Recently, numerous Deep-Learning methods have been proposed for MRI reconstruction, however, they…
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images
Jun Shi, Yuanming Zhang, Zheng Li +4
Computer-aided diagnosis (CAD) can help pathologists improve diagnostic accuracy together with consistency and repeatability for cancers. However, the CAD models trained with the h…
A channel attention based MLP-Mixer network for motor imagery decoding with EEG
Yanbin He, Zhiyang Lu, Jun Wang +1
Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task. However, these CNN-…
Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images
Zhiyang Lu, Zheng Li, Jun Wang +2
The thick-slice magnetic resonance (MR) images are often structurally blurred in coronal and sagittal views, which causes harm to diagnosis and image post-processing. Deep learning…