193 citations · 246 across the 10 of their papers we have counts for
10 papers
Coordinated Transformer with Position \& Sample-aware Central Loss for Anatomical Landmark Detection
Qikui Zhu, Yihui Bi, Danxin Wang +3
Heatmap-based anatomical landmark detection is still facing two unresolved challenges: 1) inability to accurately evaluate the distribution of heatmap; 2) inability to effectively…
Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis
Jiancheng Yang, Rui Shi, Udaranga Wickramasinghe +3
The human annotations are imperfect, especially when produced by junior practitioners. Multi-expert consensus is usually regarded as golden standard, while this annotation protocol…
RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks
Feng Sun, Ajith Kumar, Guanci Yang +4
Graph Convolutional Networks (GCNs) are widely used in many applications yet still need large amounts of labelled data for training. Besides, the adjacency matrix of GCNs is stable…
Selective Information Passing for MR/CT Image Segmentation
Qikui Zhu, Liang Li, Jiangnan Hao +5
Automated medical image segmentation plays an important role in many clinical applications, which however is a very challenging task, due to complex background texture, lack of cle…
Self-supervised Training of Graph Convolutional Networks
Qikui Zhu, Bo Du, Pingkun Yan
Graph Convolutional Networks (GCNs) have been successfully applied to analyze non-grid data, where the classical convolutional neural networks (CNNs) cannot be directly used. One s…
OASIS: One-pass aligned Atlas Set for Image Segmentation
Qikui Zhu, Bo Du, Pingkun Yan
Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task…
Multi-hop Convolutions on Weighted Graphs
Qikui Zhu, Bo Du, Pingkun Yan
Graph Convolutional Networks (GCNs) have made significant advances in semi-supervised learning, especially for classification tasks. However, existing GCN based methods have two ma…
Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation
Qikui Zhu, Bo Du, Pingkun Yan
Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segme…
On a Sparse Shortcut Topology of Artificial Neural Networks
Fenglei Fan, Dayang Wang, Hengtao Guo +4
In established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effe…
Deeply-Supervised CNN for Prostate Segmentation
Qikui Zhu, Bo Du, Baris Turkbey +2
Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion. However, the lack of clear boundary specifically at the apex and b…