Machine Learning in Clinical Neuroimaging: 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September

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.