Machine Learning in Clinical Neuroimaging

Machine Learning.- Image-to-Image Translation between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-Domain Contrastive Learning.- Multi-Shell dMRI Estimation from Single-Shell Data via Deep Learning.- A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging.- Cross-Attention for Improved Motion Correction in Brain PET.- VesselShot: Few-shot learning for cerebral blood vessel segmentation.- WaveSep: A Flexible Wavelet-based Approach for Source Separation in Susceptibility Imaging.- Joint Estimation of Neural Events and Hemodynamic Response Functions from Task fMRI via Convolutional Neural Networks.- Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation.- Clinical Applications.- Causal Sensitivity Analysis for Hidden Confounding: Modeling the Sex-Specific Role of Diet on the Aging Brain.- MixUp brain-cortical augmentations in self-supervised learning.- Brain age prediction based on head computed tomography segmentation.- Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification.- Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder.- Deep attention assisted multi-resolution networks for the segmentation of white matter hyperintensities in postmortem MRI scans .- Stroke outcome and evolution prediction from CT brain using a spatiotemporal diffusion autoencoder.- Morphological versus Functional Network Organization: A Comparison Between Structural Covariance Networks and Probabilistic Functional Modes.