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Machine Learning in Clinical Neuroimaging de Ahmed Abdulkadir

Machine Learning in Clinical Neuroimaging: Lecture Notes in Computer Science, cartea 13001

Editat de Ahmed Abdulkadir, Seyed Mostafa Kia, Mohamad Habes, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Thomas Wolfers
en Limba Engleză Paperback – 23 sep 2021
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.


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Specificații

ISBN-13: 9783030875855
ISBN-10: 3030875857
Pagini: 188
Ilustrații: XI, 176 p. 65 illus., 53 illus. in color.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.3 kg
Ediția:1st edition 2021
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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.