Graph Learning in Medical Imaging : first International Workshop, GLMI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / Daoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu (eds.)
Intro; Preface; Organization; Contents; Graph Hyperalignment for Multi-subject fMRI Functional Alignment; 1 Introduction; 2 Graph Hyperalignment; 3 Optimization; 4 Experiments; 5 Conclusion; References; Interactive 3D Segmentation Editing and Refinement via Gated Graph Neural Networks; 1 Introduction; 2 Method; 2.1 Graph Gated Neural Networks; 3 Results and Discussion; 4 Conclusion; References; Adaptive Thresholding of Functional Connectivity Networks for fMRI-Based Brain Disease Analysis; 1 Introduction; 2 Method; 2.1 Subjects and Image Preprocessing
2.2 Weight Distribution Based Thresholding Method3 Experiments; 4 Conclusion; References; Graph-Kernel-Based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification; 1 Introduction; 2 Method; 2.1 Subjects and Image Preprocessing; 2.2 Proposed Graph-Kernel-Based Multi-task Structured Feature Selection; 2.3 gk-MTSFS Based Learning Framework; 3 Experiments; 3.1 Experimental Setup; 3.2 Classification Performance; 3.3 Effect of Regularization Parameters; 4 Conclusion; References
Linking Convolutional Neural Networks with Graph Convolutional Networks: Application in Pulmonary Artery-Vein Separation1 Introduction; 2 Methods; 2.1 Graph Convolution Networks; 2.2 Linking CNN with GCN; 2.3 Application to Pulmonary Artery-Vein Separation; 3 Experiments; 4 Results; 5 Discussion and Conclusion; References; Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction; 1 Introduction; 2 Methods; 2.1 MRF Dictionary; 2.2 Dimensionality Reduction via Hierarchical SNE; 2.3 Registration of the Embeddings; 3 Experiments and Results
3.1 Embedding Stability3.2 Dictionary Length; 3.3 Dependence of Produced Embeddings on Dictionary Size; 4 Discussion and Conclusions; References; Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients; 1 Introduction and Related Work; 1.1 Contributions; 2 Methods; 2.1 Descriptor Learning on Point Sets with Dynamic Graph CNNs; 2.2 Feature-Based Coherent Point Drift; 3 Experiments; 4 Results and Discussion; 5 Conclusion; References; Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography
1 Introduction2 Data; 3 Methods; 3.1 Surface Mesh; 3.2 GCN Architecture; 4 Experiments and Results; 5 Discussion and Conclusions; References; Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI; 1 Introduction; 2 Method; 3 Experiment and Results; 4 Conclusion; References; Multi-scale Graph Convolutional Network for Mild Cognitive Impairment Detection; 1 Introduction; 2 Methodology; 2.1 Data Preprocessing; 2.2 Dynamic High-Order FCNs Construction; 2.3 Feature Extraction; 2.4 Graph Construction; 2.5 Spectral Convolution
