Search Results for author:
Found 10 papers, 6 papers with code
Coordinated Transformer with Position \& Sample-aware Central Loss for Anatomical Landmark Detection
Heatmap-based anatomical landmark detection is still facing two unresolved challenges: 1) inability to accurately evaluate the distribution of heatmap; 2) inability to effectively exploit global spatial structure information.
Neural Annotation Refinement: Development of a New 3D Dataset for Adrenal Gland Analysis
Besides, we develop a new Adrenal gLand ANalysis (ALAN) dataset with the proposed NeAR, where each case consists of a 3D shape of adrenal gland and its diagnosis label (normal vs. abnormal) assigned by experts.
RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are widely used in many applications yet still need large amounts of labelled data for training.
Selective Information Passing for MR/CT Image Segmentation
However, not all the feature maps transmitted by those connections contribute positively to the network performance.
Self-supervised Training of Graph Convolutional Networks
Furthermore, the adjacency matrix is usually pre-defined and stationary, which makes the data augmentation strategies cannot be employed on the constructed graph structures data to augment the amount of training data.
OASIS: One-pass aligned Atlas Set for Image Segmentation
Furthermore, instead of using image based similarity for label fusion, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion.
Multi-hop Convolutions on Weighted Graphs
To address the above weaknesses, in this paper, we propose a new method of multi-hop convolutional network on weighted graphs.
Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation
To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss (BWL) is proposed.
On a Sparse Shortcut Topology of Artificial Neural Networks
In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology.
Deeply-Supervised CNN for Prostate Segmentation
Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion.
