Buch, Englisch, Band 13001, 176 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 295 g
4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
Buch, Englisch, Band 13001, 176 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 295 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-030-87585-5
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.