Information Processing in Medical Imaging

Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG.- A Novel Sparse Overlapping Modularized Gaussian Graphical Model for Functional Connectivity Estimation.- White Matter Imaging.- Asymmetry Spectrum Imaging for Baby Diffusion Tractography.- A Fast Fiber k-Nearest-Neighbor Algorithm with Application to Group-Wise White Matter Topography Analysis.- Posters.- 3D Organ Shape Reconstruction from Topogram Images.- A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation.- A Graph Model of the Lungs with MorphologyBased Structure for Tuberculosis Type Classification.- A Longitudinal Model for Tau Aggregation in Alzheimers Disease Based on Structural Connectivity.- Accurate Nuclear Segmentation with Center Vector Encoding.- Bayesian Longitudinal Modeling of Early Stage Parkinsons Disease Using DaTscan Images.- Brain Tumor Segmentation on MRI with Missing Modalities.- Contextual Fibre Growthto Generate Realistic Axonal Packing for Diffusion MRI Simulation.- DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction.- ECKO: Ensemble of Clustered Knockoffs for Robust Multivariate Inference on fMRI Data.- FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms.- Graph Convolutional Nets for Tool Presence Detection in Surgical Videos.- High-Order Oriented Cylindrical Flux for Curvilinear Structure Detection and Vessel Segmentation.- Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network.- Learning a Conditional Generative Model for Anatomical Shape Analysis.- Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness.- Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data.- Riemannian Geometry Learning for Disease Progression Modelling.- Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model.- Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices with Applications to Neuroimaging.- Simultaneous Spatial-temporal Decomposition of Connectome-Scale Brain Networks by Deep Sparse Recurrent Auto-Encoders.- Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention.- A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces.- A Geometric Framework for Feature Mappings in Multimodal Fusion of Brain Image Data.- A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data.- A Model for Elastic Evolution on Foliated Shapes.- Analyzing Mild Cognitive Impairment Progression via Multi-view Structural Learning.- New Graph-Blind Convolutional Network for Brain Connectome Data Analysis.- CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation.- Data-Driven Model Order Reduction For Diffeomorphic Image Registration.-DGR-Net: Deep Groupwise Registration of Multispectral Images.- Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery.- Generalizations of Ripleys K-Function with Application to Space Curves.- Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates.- InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-Contrast Microstructural MRI.- Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases.- Learning-Based Optimization of the Under-Sampling Pattern in MRI.- Melanoma Recognition via Visual Attention.- Nonlinear Markov Random Fields Learned via Backpropagation.- Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler.- SHAMANN: Shared Memory Augmented Neural Networks.- Signet Ring Cell Detection With a Semi-supervisedLearning Framework.- Spherical U-Net on Cortical Surfaces: Methods and Applications.- Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis.