Machine Learning in Clinical Neuroimaging

Chapter 1 Image-to-Image Translation Between Tau Pathology and Neuronal Metabolism PET in Alzheimer Disease with Multi-domain Contrastive Learning

Chapter 2 Multi-shell dMRI Estimation from Single-Shell Data via Deep Learning

Chapter 3 A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging

Chapter 4 Cross-Attention for Improved Motion Correction in Brain PET

Chapter 5 VesselShot: Few-shot Learning for Cerebral Blood Vessel Segmentation

Chapter 6 WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging

Chapter 7 Joint Estimation of Neural Events and Hemodynamic Response Functions from Task fMRI via Convolutional Neural Networks

Chapter 8 Learning Sequential Information in Task-Based fMRI for Synthetic Data Augmentation

Chapter 10 MixUp Brain-Cortical Augmentations in Self-supervised Learning

Chapter 11 Brain Age Prediction Based on Head Computed Tomography Segmentation

Chapter 12 Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification

Chapter 13 Copy Number Variation Informs fMRI-Based Prediction of Autism Spectrum Disorder

Chapter 14 Deep Attention Assisted Multi-resolution Networks for the Segmentation of White Matter Hyperintensities in Postmortem MRI Scans

Chapter 15 Stroke Outcome and Evolution Prediction from CT Brain Using a Spatiotemporal Diffusion Autoencoder

Chapter 16 Morphological Versus Functional Network Organization: A Comparison Between Structural Covariance Networks and Probabilistic Functional Modes