E-raamat: Medical Image Computing and Computer Assisted Intervention - MICCAI 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part VII

Brain Development and Atlases.- A New Metric for Characterizing Dynamic
Redundancy of Dense Brain Chronnectome and Its Application to Early Detection
of Alzheimer's Disease.- A computational framework for dissociating
development-related from individually variable flexibility in regional
modularity assignment in early infancy.- Domain-invariant Prior Knowledge
Guided Attention Networks for Robust Skull Stripping of Developing Macaque
Brains.- Parkinson's Disease Detection from fMRI-derived Brainstem Regional
Functional Connectivity Networks.- Persistent Feature Analysis of Multimodal
Brain Networks Using Generalized Fused Lasso for EMCI Identification.-
Recovering Brain Structural Connectivity from Functional Connectivity via
Multi-GCN based Generative Adversarial Network.- From Connectomic to
Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from
Resting-state Functional Connectivity.- Disentangled Intensive Triplet
Autoencoder for Infant Functional Connectome Fingerprinting.- COVLET:
Covariance-based Wavelet-like Transform for Statistical Analysis of Brain
Characteristics in Children.- Species-Shared and -Specific Structural
Connections Revealed by Dirty Multi-Task Regression.- Self-weighted
Multi-Task Learning for Subjective Cognitive Decline Diagnosis.- Unified
Brain Network with Functional and Structural Data.- Integrating Similarity
Awareness and Adaptive Calibration in Graph Convolution Network to Predict
Disease.- Infant Cognitive Scores Prediction With Multi-stream
Attention-based Temporal Path Signature Features.- Masked Multi-Task Network
for Case-level Intracranial Hemorrhage Classification in Brain CT Volumes.-
Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating
Connectional Brain Templates.- Supervised Multi-topology Network
Cross-diffusion for Population-Driven Brain Network Atlas Estimation.-
Partial Volume Segmentation of Brain MRI Scans of any Resolution and
Contrast.- BDB-Net: Boundary-enhanced DualBranch Network for Whole Brain
Segmentation.- Brain Age Estimation From MRI Using a Two-Stage Cascade
Network with a Ranking Loss.- Context-Aware Refinement Network Incorporating
Structural Connectivity Prior for Brain Midline Delineation.- Optimizing
Visual Cortex Parameterization with Error-Tolerant Teichmüller Map in
Retinotopic Mapping.- Multi-Scale Enhanced Graph Convolutional Network for
Early Mild Cognitive Impairment Detection.- Construction of Spatiotemporal
Infant Cortical Surface Functional Templates.- DWI and Tractography.- Tract
Dictionary Learning for Fast and Robust Recognition of Fiber Bundles.-
Globally Optimized Super-Resolution of Diffusion MRI Data via Fiber
Continuity.- White Matter Tract Segmentation with Self-supervised Learning.-
Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph
Convolutional Neural Networks.- Tractogram filtering of anatomically
non-plausible fibers with geometric deep learning.- Unsupervised Deep
Learning for Susceptibility Distortion Correction in Connectome Imaging.-
Hierarchical geodesic modeling on the diffusion orientation distribution
function for longitudinal DW-MRI analysis.- TRAKO: Efficient Transmission of
Tractography Data for Visualization.- Spatial Semantic-Preserving Latent
Space Learning for Accelerated DWI Diagnostic Report Generation.-
Trajectories from Distribution-valued Functional Curves: A Unified
Wasserstein Framework.- Characterizing Intra-Soma Diffusion with Spherical
Mean Spectrum Imaging.- Functional Brain Networks.- Estimating Common
Harmonic Waves of Brain Networks on Stiefel Manifold.- Neural Architecture
Search for Optimization of Spatial-temporal Brain Network Decomposition.-
Attention-Guided Deep Graph Neural Network for Longitudinal Alzheimers
Disease Analysis.- Enriched Representation Learning in Resting-State fMRI for
Early MCI Diagnosis.- Whole MILC: generalizing learned dynamics across tasks,
datasets, and populations.- A physics-informed geometric learning model for
pathological tau spread in Alzheimer's disease.- A deep pattern recognition
approach for inferring respiratory volume fluctuations from fMRI data.- A
Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity
for Predicting Spectrum-Level Deficits in Autism.- Poincare embedding reveals
edge-based functional networks of the brain.- The constrained network-based
statistic: a new level of inference for neuroimaging.- Learning Personal
Representations from fMRIby Predicting Neurofeedback Performance.- A 3D
Convolutional Encapsulated Long Short-Term Memory (3DConv-LSTM) Model for
Denoising fMRI Data.- Detecting Changes of Functional Connectivity by Dynamic
Graph Embedding Learning.- Discovering Functional Brain Networks with 3D
Residual Autoencoder (ResAE).- Spatiotemporal Attention Autoencoder (STAAE)
for ADHD Classification.- Global Diffeomorphic Phase Alignment of Time-series
from Resting-state fMRI Data.- Spatio-Temporal Graph Convolution for
Resting-State fMRI Analysis.- A shared neural encoding model for the
prediction of subject-specific fMRI response.- Neuroimaging.- Topology-Aware
Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs
from a Single Brain Graph.- Edge-variational Graph Convolutional Networks for
Uncertainty-aware Disease Prediction.- Fisher-Rao Regularized Transport
Analysis of the Glymphatic System and Waste Drainage.- Joint Neuroimage
Synthesis and Representation Learning for Conversion Prediction of Subjective
Cognitive Decline.- Differentiable Deconvolution for Improved Stroke
Perfusion Analysis.- Spatial Similarity-Aware Learning and Fused Deep
Polynomial Network for Detection of Obsessive-Compulsive Disorder.- Deep
Representation Learning For Multimodal Brain Networks.- Pooling Regularized
Graph Neural Network for fMRI Biomarker Analysis.- Patch-based abnormality
maps for improved deep learning-based classification of Huntington's
disease.- A Deep Spatial Context Guided Framework for Infant Brain
Subcortical Segmentation.- Modelling the Distribution of 3D Brain MRI using a
2D Slice VAE.- Spatial Component Analysis to Mitigate Multiple Testing in
Voxel-Based Analysis.- MAGIC: Multi-scale Heterogeneity Analysis and
Clustering for Brain Diseases.- PIANO: Perfusion Imaging via
Advection-diffusion.- Hierarchical Bayesian Regression for Multi-Site
Normative Modeling of Neuroimaging Data.- Image-level Harmonization of
Multi-Site Data using Image-and-Spatial Transformer Networks.- A Disentangled
Latent Space for Cross-Site MRI Harmonization.- Automated Acquisition
Planning for Magnetic Resonance Spectroscopy in Brain Cancer.- Positron
Emission Tomography.- Simultaneous Denoising and Motion Estimation for
Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate
Consistency Learning.- Lymph Node Gross Tumor Volume Detection and
Segmentation via Distance-based Gating using 3D CT/PET Imaging in
Radiotherapy.- Multi-Modality Information Fusionfor Radiomics-based Neural
Architecture Search.- Lymph Node Gross Tumor Volume Detection in Oncology
Imaging via Relationship Learning Using Graph Neural Network.- Rethinking PET
Image Reconstruction: Ultra-Low-Dose, Sinogram and Deep Learning.- Clinically
Translatable Direct Patlak Reconstruction from Dynamic PET with Motion
Correction Using Convolutional Neural Network.- Collimatorless Scintigraphy
for Imaging Extremely Low Activity Targeted Alpha Therapy (TAT) with Weighted
Robust Least Square (WRLS).