Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / edited by Daoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu.

Format:

Book

Contributor:

Zhang, Daoqiang, editor.

Zhou, Luping, editor.

Jie, Biao, editor.

Liu, Mingxia, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11849.

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11849

Language:

English

Subjects (All):

Artificial intelligence.

Optical data processing.

Pattern perception.

Application software.

Artificial Intelligence.

Image Processing and Computer Vision.

Pattern Recognition.

Computer Appl. in Social and Behavioral Sciences.

Local Subjects:

Artificial Intelligence.

Image Processing and Computer Vision.

Pattern Recognition.

Computer Appl. in Social and Behavioral Sciences.

Physical Description:

1 online resource (IX, 182 pages) : 87 illustrations, 68 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 21 full papers presented were carefully reviewed and selected from 42 submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging.

Contents:

Graph Hyperalignment for Multi-Subject fMRI Functional Alignment

Interactive 3D Segmentation Editing and Refinement via Gated Graph Neural Networks

Adaptive Thresholding of Functional Connectivity Networks for fMRI-based Brain Disease Analysis

Graph-kernel-based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification

Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation

Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction

Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients

Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography

Triplet Graph Convolutional Network forMulti-scale Analysis of Functional Connectivityusing Functional MRI

Multi-Scale Graph Convolutional Network for Mild Cognitive Impairment Detection

DeepBundle: Fiber Bundle Parcellation With Graph CNNs

Identification of Functional Connectivity Features in Depression Subtypes Using a Data-Driven Approach

Movie-watching fMRI Reveals Inter-subject Synchrony Alteration in Functional Brain Activity in ADHD

Weakly- and Semi- Supervised Graph CNN for identifying Basal Cell Carcinoma on Pathological images

Geometric Brain Surface Network For Brain Cortical Parcellation

Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images using 3D Mask R-CNN

Discriminative-Region-Aware Residual Network for Adolescent Brain Structure and Cognitive Development Analysis

Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram

OCD Diagnosis via Smoothing Sparse Network and Stacked Sparse Auto-Encoder Learning

A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism

CNS: CycleGAN-assisted Neonatal Segmentation Model for Cross-Datasets.

Other Format:

Printed edition:

ISBN:

978-3-030-35817-4

9783030358174

Access Restriction:

Restricted for use by site license.