Machine Learning in Clinical Neuroimaging (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
176
Utgivningsdatum
2021-09-23
Upplaga
1st ed. 2021
Förlag
Springer Nature Switzerland AG
Medarbetare
Abdulkadir, Ahmed (ed.), Kia, Seyed Mostafa (ed.), Habes, Mohamad (ed.), Wolfers, Thomas (ed.), Rondina, Jane Maryam (ed.), Tax, Chantal (ed.), Kumar, Vinod (ed.)
Illustrationer
53 Illustrations, color; 12 Illustrations, black and white; XI, 176 p. 65 illus., 53 illus. in color
Dimensioner
234 x 156 x 10 mm
Vikt
272 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030875855
Machine Learning in Clinical Neuroimaging (häftad)

Machine Learning in Clinical Neuroimaging

4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

Häftad Engelska, 2021-09-23
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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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Innehållsförteckning

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.