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Author GLMI (Workshop) (1st : 2019 : Shenzhen Shi, China)

Title 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.)
Published Cham : Springer, 2020


Description 1 online resource (191 pages)
Series Lecture notes in computer science ; 11849
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
Lecture notes in computer science ; 11849.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
Contents 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
Notes International conference proceedings
2.6 Inception Modules
Includes author index
Print version record
Subject Diagnostic imaging -- Digital techniques -- Congresses
Diagnostic imaging -- Digital techniques.
Genre/Form Conference papers and proceedings.
Conference papers and proceedings.
Actes de congrès.
Form Electronic book
Author Zhang, Daoqiang.
Zhou, Luping.
Jie, Biao
Liu, Mingxia (Research instructor)
International Conference on Medical Image Computing and Computer-Assisted Intervention (22nd : 2019 : Shenzhen Shi, China) jointly held conference
ISBN 9783030358174
Other Titles GLMI 2019