| 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. |
| 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 |
| Ãndice Dewey: |
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. |
| En lÃnea: |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
| Link: |
https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i |
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.
| 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 |
| Ãndice Dewey: |
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. |
| En lÃnea: |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
| Link: |
https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i |
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Machine Learning in Clinical Neuroimaging
