Machine Learning in Clinical Neuroimaging 4th International Workshop MLCN 2021 Held in Conjunction with MICCAI 2021 Strasbourg France September Lecture Notes in Computer Science 13001 by Ahmed Abdulkadir (Editor) , Seyed Mostafa Kia (editor), Mohamad Habes (editor), Vinod Kumar (editor), Jane Maryam Rondina (editor), Chantal Tax (editor), Thomas Wolfers (editor) 3030875857 9783030875855

Machine Learning in Clinical Neuroimaging 4th International Workshop MLCN 2021 Held in Conjunction with MICCAI 2021 Strasbourg France September Lecture Notes in Computer Science 13001 Ahmed Abdulkadir (Editor) – Ebook Instant Download/Delivery ISBN(s): 9783030875855,3030875857

Product details:

• ISBN 10:3030875857
• ISBN 13:9783030875855
• Author:Ahmed Abdulkadir (editor), Seyed Mostafa Kia (editor), Mohamad Habes (editor), Vinod Kumar (editor), Jane Maryam Rondina (editor), Chantal Tax (editor), Thomas Wolfers (editor)

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.

Table contents:

1. Computational Anatomy
2. Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer’s Disease Pathology Using Ex vivo Imaging
3. Distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep Networks
4. Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows
5. Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients
6. MRI Image Registration Considerably Improves CNN-Based Disease Classification
7. Dynamic Sub-graph Learning for Patch-Based Cortical Folding Classification
8. Detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models
9. PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction
10. Multi-modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network
11. Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance
12. Brain Networks and Time Series
13. Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation
14. Deep Stacking Networks for Conditional Nonlinear Granger Causal Modeling of fMRI Data
15. Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling
16. Structure-Function Mapping via Graph Neural Networks
17. Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
18. H3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning
19. Constrained Learning of Task-Related and Spatially-Coherent Dictionaries from Task fMRI D

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