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Autor Abdulkadir, Ahmed |
Documentos disponibles escritos por este autor (2)
Crear una solicitud de compra Refinar búsquedaMachine Learning in Clinical Neuroimaging / Abdulkadir, Ahmed ; Kia, Seyed Mostafa ; Habes, Mohamad ; Kumar, Vinod ; Rondina, Jane Maryam ; Tax, Chantal ; Wolfers, Thomas
TÃtulo : Machine Learning in Clinical Neuroimaging : 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings Tipo de documento: documento electrónico Autores: Abdulkadir, Ahmed, ; Kia, Seyed Mostafa, ; Habes, Mohamad, ; Kumar, Vinod, ; Rondina, Jane Maryam, ; Tax, Chantal, ; Wolfers, Thomas, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XI, 176 p. 65 ilustraciones, 53 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-87586-2 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Procesamiento de imágenes Visión por computador Inteligencia artificial Bioinformática Imágenes por computadora visión reconocimiento de patrones y gráficos BiologÃa Computacional y de Sistemas Clasificación: 6 Resumen: Este libro constituye las actas arbitradas del 4to Taller Internacional sobre Aprendizaje Automático en Neuroimagen ClÃnica, MLCN 2021, celebrado el 27 de septiembre de 2021, junto con MICCAI 2021. El taller se llevó a cabo virtualmente debido a la pandemia de COVID-19. Los 17 artÃculos presentados en este libro fueron cuidadosamente revisados ​​y seleccionados entre 27 presentaciones. Se organizaron en secciones temáticas denominadas: anatomÃa computacional y redes cerebrales y series temporales. Nota de contenido: Computational Anatomy -- Unfolding the medial temporal lobe cortex to characterize neurodegeneration due to Alzheimer's disease pathology using ex vivo imaging -- Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks -- Towards Self-Explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows -- Patch vs. global image-based unsupervised anomaly detection in MR brain scans of early Parkinsonian patients -- MRI image registration considerably improves CNN-based disease classification -- Dynamic Sub-graph Learning for Patch-based Cortical Folding Classification -- Detection of abnormal folding patterns with unsupervised deep generative models -- PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction -- Multi-Modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network -- Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance -- Brain Networks and Time Series -- Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation -- Deep Stacking Networks for Conditional Nonlinear Granger Causal Modeling of fMRI Data -- Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling -- Structure-Function Mapping via Graph Neural Networks -- Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity -- H3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning -- Constrained Learning of Task-related and Spatially-Coherent Dictionaries from Task fMRI Data. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021, held on September 27, 2021, in conjunction with MICCAI 2021. The workshop was held virtually due to the COVID-19 pandemic. The 17 papers presented in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections named: computational anatomy and brain networks and time series. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning in Clinical Neuroimaging : 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings [documento electrónico] / Abdulkadir, Ahmed, ; Kia, Seyed Mostafa, ; Habes, Mohamad, ; Kumar, Vinod, ; Rondina, Jane Maryam, ; Tax, Chantal, ; Wolfers, Thomas, . - 1 ed. . - [s.l.] : Springer, 2021 . - XI, 176 p. 65 ilustraciones, 53 ilustraciones en color.
ISBN : 978-3-030-87586-2
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Procesamiento de imágenes Visión por computador Inteligencia artificial Bioinformática Imágenes por computadora visión reconocimiento de patrones y gráficos BiologÃa Computacional y de Sistemas Clasificación: 6 Resumen: Este libro constituye las actas arbitradas del 4to Taller Internacional sobre Aprendizaje Automático en Neuroimagen ClÃnica, MLCN 2021, celebrado el 27 de septiembre de 2021, junto con MICCAI 2021. El taller se llevó a cabo virtualmente debido a la pandemia de COVID-19. Los 17 artÃculos presentados en este libro fueron cuidadosamente revisados ​​y seleccionados entre 27 presentaciones. Se organizaron en secciones temáticas denominadas: anatomÃa computacional y redes cerebrales y series temporales. Nota de contenido: Computational Anatomy -- Unfolding the medial temporal lobe cortex to characterize neurodegeneration due to Alzheimer's disease pathology using ex vivo imaging -- Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks -- Towards Self-Explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows -- Patch vs. global image-based unsupervised anomaly detection in MR brain scans of early Parkinsonian patients -- MRI image registration considerably improves CNN-based disease classification -- Dynamic Sub-graph Learning for Patch-based Cortical Folding Classification -- Detection of abnormal folding patterns with unsupervised deep generative models -- PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction -- Multi-Modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network -- Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance -- Brain Networks and Time Series -- Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation -- Deep Stacking Networks for Conditional Nonlinear Granger Causal Modeling of fMRI Data -- Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling -- Structure-Function Mapping via Graph Neural Networks -- Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity -- H3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning -- Constrained Learning of Task-related and Spatially-Coherent Dictionaries from Task fMRI Data. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021, held on September 27, 2021, in conjunction with MICCAI 2021. The workshop was held virtually due to the COVID-19 pandemic. The 17 papers presented in this book were carefully reviewed and selected from 27 submissions. They were organized in topical sections named: computational anatomy and brain networks and time series. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology : Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings / Tipo de documento: documento electrónico Autores: Kia, Seyed Mostafa, ; Mohy-ud-Din, Hassan, ; Abdulkadir, Ahmed, ; Bass, Cher, ; Habes, Mohamad, ; Rondina, Jane Maryam, ; Tax, Chantal, ; Wang, Hongzhi, ; Wolfers, Thomas, ; Rathore, Saima, ; Ingalhalikar, Madhura, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XVIII, 305 p. 8 ilustraciones ISBN/ISSN/DL: 978-3-030-66843-3 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del Tercer Taller Internacional sobre Aprendizaje Automático en Neuroimagen ClÃnica, MLCN 2020, y el Segundo Taller Internacional sobre Radiogenómica en NeurooncologÃa, RNO-AI 2020, celebrado en conjunto con MICCAI 2020, en Lima, Perú. en octubre de 2020.* Para MLCN 2020, se aceptaron para publicación 18 artÃculos de 28 presentados. Los artÃculos aceptados presentan contribuciones novedosas tanto en el desarrollo de nuevos métodos de aprendizaje automático como en aplicaciones de métodos existentes para resolver problemas desafiantes en neuroimagen clÃnica. Para RNO-AI 2020, se aceptaron las 8 presentaciones para su publicación. Se centran en abordar los problemas de aplicar el aprendizaje automático a conjuntos de datos de neuroimagen clÃnica grandes y multisitio. El taller tenÃa como objetivo reunir a expertos tanto en aprendizaje automático como en neuroimagen clÃnica para discutir y, con suerte, superar los desafÃos existentes del aprendizaje automático aplicado en la neurociencia clÃnica. *Los talleres se realizaron de manera virtual debido a la pandemia de COVID-19. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology : Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings / [documento electrónico] / Kia, Seyed Mostafa, ; Mohy-ud-Din, Hassan, ; Abdulkadir, Ahmed, ; Bass, Cher, ; Habes, Mohamad, ; Rondina, Jane Maryam, ; Tax, Chantal, ; Wang, Hongzhi, ; Wolfers, Thomas, ; Rathore, Saima, ; Ingalhalikar, Madhura, . - 1 ed. . - [s.l.] : Springer, 2020 . - XVIII, 305 p. 8 ilustraciones.
ISBN : 978-3-030-66843-3
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Clasificación: 006.37 Resumen: Este libro constituye las actas arbitradas del Tercer Taller Internacional sobre Aprendizaje Automático en Neuroimagen ClÃnica, MLCN 2020, y el Segundo Taller Internacional sobre Radiogenómica en NeurooncologÃa, RNO-AI 2020, celebrado en conjunto con MICCAI 2020, en Lima, Perú. en octubre de 2020.* Para MLCN 2020, se aceptaron para publicación 18 artÃculos de 28 presentados. Los artÃculos aceptados presentan contribuciones novedosas tanto en el desarrollo de nuevos métodos de aprendizaje automático como en aplicaciones de métodos existentes para resolver problemas desafiantes en neuroimagen clÃnica. Para RNO-AI 2020, se aceptaron las 8 presentaciones para su publicación. Se centran en abordar los problemas de aplicar el aprendizaje automático a conjuntos de datos de neuroimagen clÃnica grandes y multisitio. El taller tenÃa como objetivo reunir a expertos tanto en aprendizaje automático como en neuroimagen clÃnica para discutir y, con suerte, superar los desafÃos existentes del aprendizaje automático aplicado en la neurociencia clÃnica. *Los talleres se realizaron de manera virtual debido a la pandemia de COVID-19. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
