Machine Learning in Clinical Neuroimaging : 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings / edited by Ahmed Abdulkadir, Seyed Mostafa Kia, Mohamad Habes, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Thomas Wolfers.

Format:

Book

Contributor:

Abdulkadir, Ahmed, Editor.

Kia, Seyed Mostafa, Editor.

Habes, Mohamad, Editor.

Kumar, Vinod, Editor.

Rondina, Jane Maryam., Editor.

Tax, Chantal., Editor.

Wolfers, Thomas., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 13001

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 13001

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Artificial intelligence.

Bioinformatics.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computational and Systems Biology.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computational and Systems Biology.

Physical Description:

1 online resource (XI, 176 pages) : 65 illustrations, 53 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-87586-2

9783030875862

Access Restriction:

Restricted for use by site license.