| TÃtulo : |
Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings |
| Tipo de documento: |
documento electrónico |
| Autores: |
Zhang, Daoqiang, ; Zhou, Luping, ; Jie, Biao, ; Liu, Mingxia, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2019 |
| Número de páginas: |
IX, 182 p. 87 ilustraciones, 68 ilustraciones en color. |
| ISBN/ISSN/DL: |
978-3-030-35817-4 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Palabras clave: |
Sistemas de reconocimiento de patrones Aplicación informática en ciencias sociales y del comportamiento Reconocimiento de patrones automatizado Visión por computador Inteligencia artificial Ciencias sociales Procesamiento de datos |
| Clasificación: |
|
| Resumen: |
Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Aprendizaje de Gráficos en Imágenes Médicas, GLMI 2019, celebrado junto con MICCAI 2019 en Shenzhen, China, en octubre de 2019. Los 21 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 42 presentaciones. Los artÃculos se centran en las principales tendencias y desafÃos del aprendizaje de gráficos en imágenes médicas y presentan trabajos originales destinados a identificar nuevas técnicas de vanguardia y sus aplicaciones en imágenes médicas. |
| Nota de contenido: |
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. |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings [documento electrónico] / Zhang, Daoqiang, ; Zhou, Luping, ; Jie, Biao, ; Liu, Mingxia, . - 1 ed. . - [s.l.] : Springer, 2019 . - IX, 182 p. 87 ilustraciones, 68 ilustraciones en color. ISBN : 978-3-030-35817-4 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
| Palabras clave: |
Sistemas de reconocimiento de patrones Aplicación informática en ciencias sociales y del comportamiento Reconocimiento de patrones automatizado Visión por computador Inteligencia artificial Ciencias sociales Procesamiento de datos |
| Clasificación: |
|
| Resumen: |
Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Aprendizaje de Gráficos en Imágenes Médicas, GLMI 2019, celebrado junto con MICCAI 2019 en Shenzhen, China, en octubre de 2019. Los 21 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 42 presentaciones. Los artÃculos se centran en las principales tendencias y desafÃos del aprendizaje de gráficos en imágenes médicas y presentan trabajos originales destinados a identificar nuevas técnicas de vanguardia y sus aplicaciones en imágenes médicas. |
| Nota de contenido: |
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. |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
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