Machine Learning in Clinical Neuroimaging

Morphometry.- Joint Reconstruction and Parcellation of Cortical Surfaces.- A Study of Demographic Bias in CNN-based Brain MR Segmentation.- Volume is All You Need: Improving Multi-task Multiple Instance Learning for WMH Segmentation and Severity Estimation.- Self-Supervised Test-Time Adaptation for Medical Image Segmentation.- Accurate Hippocampus Segmentation Based on Self-Supervised Learning with Fewer Labeled Data.- Concurrent Ischemic Lesion Age Estimation and Segmentation of CT Brain Using a Transformer-based Network.- Weakly Supervised Intracranial Hemorrhage Segmentation using Hierarchical Combination of Attention Maps from a Swin Transformer.- Boundary Distance Loss for Intra-/Extra-meatal Segmentation of Vestibular Schwannoma.- Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-protocol Volumetric Consistency.-      Diagnostics, Aging, and Neurodegeneration.- Non-parametric ODE-based Disease Progression Model of Brain Biomarkers in Alzheimer's Disease.- Lifestyle Factors that Promote Brain Structural Resilience in Individuals with Genetic Risk Factors for Dementia.- Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging.- Augmenting Magnetic Resonance Imaging with Tabular Features for Enhanced and Interpretable Medial Temporal Lobe Atrophy Prediction.- Automatic Lesion Analysis for Increased Efficiency in Outcome Prediction of Traumatic Brain Injury.- Autism Spectrum Disorder Classification Based on Interpersonal Neural Synchrony: Can Classification be Improved by Dyadic Neural Biomarkers Using Unsupervised Graph Representation Learning?.- fMRI-S4: Learning Short- and Long-range Dynamic fMRI Dependencies Using 1D Convolutions and State Space Models.- Data Augmentation via Partial Nonlinear Registration for Brain-age Prediction.