Table of Contents

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   Book Overview
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   Chapter 1 A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration
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   Chapter 2 Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology
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   Chapter 3 Semi-supervised and Task-Driven Data Augmentation
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   Chapter 4 Analyzing Brain Morphology on the Bag-of-Features Manifold
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   Chapter 5 Modeling and Inference of Spatio-Temporal Protein Dynamics Across Brain Networks
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   Chapter 6 InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
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   Chapter 7 Adaptive Graph Convolution Pooling for Brain Surface Analysis
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   Chapter 8 On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging
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    Chapter 9 A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging
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    Chapter 10 Improved Disease Classification in Chest X-Rays with Transferred Features from Report Generation
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    Chapter 11 Limited Angle Tomography Reconstruction: Synthetic Reconstruction via Unsupervised Sinogram Adaptation
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    Chapter 12 Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
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    Chapter 13 Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia
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    Chapter 14 Minimizing Non-holonomicity: Finding Sheets in Fibrous Structures
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    Chapter 15 Learning Low-Dimensional Representations of Shape Data Sets with Diffeomorphic Autoencoders
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    Chapter 16 Diffeomorphic Medial Modeling
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    Chapter 17 Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing
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    Chapter 18 Local Optimal Transport for Functional Brain Template Estimation
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    Chapter 19 Unsupervised Deformable Registration for Multi-modal Images via Disentangled Representations
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    Chapter 20 Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting
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    Chapter 21 Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces
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    Chapter 22 Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG
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    Chapter 23 A Novel Sparse Overlapping Modularized Gaussian Graphical Model for Functional Connectivity Estimation
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    Chapter 24 Asymmetry Spectrum Imaging for Baby Diffusion Tractography
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    Chapter 25 A Fast Fiber k-Nearest-Neighbor Algorithm with Application to Group-Wise White Matter Topography Analysis
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    Chapter 26 3D Organ Shape Reconstruction from Topogram Images
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    Chapter 27 A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation
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    Chapter 28 A Graph Model of the Lungs with Morphology-Based Structure for Tuberculosis Type Classification
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    Chapter 29 A Longitudinal Model for Tau Aggregation in Alzheimer’s Disease Based on Structural Connectivity
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    Chapter 30 Accurate Nuclear Segmentation with Center Vector Encoding
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    Chapter 31 Bayesian Longitudinal Modeling of Early Stage Parkinson’s Disease Using DaTscan Images
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    Chapter 32 Brain Tumor Segmentation on MRI with Missing Modalities
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    Chapter 33 Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation
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    Chapter 34 DeepCenterline: A Multi-task Fully Convolutional Network for Centerline Extraction
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    Chapter 35 ECKO: Ensemble of Clustered Knockoffs for Robust Multivariate Inference on fMRI Data
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    Chapter 36 Graph Convolutional Nets for Tool Presence Detection in Surgical Videos
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    Chapter 37 High-Order Oriented Cylindrical Flux for Curvilinear Structure Detection and Vessel Segmentation
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    Chapter 38 Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network
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    Chapter 39 Learning a Conditional Generative Model for Anatomical Shape Analysis
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    Chapter 40 Manifold Exploring Data Augmentation with Geometric Transformations for Increased Performance and Robustness
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    Chapter 41 Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data
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    Chapter 42 Riemannian Geometry Learning for Disease Progression Modelling
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    Chapter 43 Semi-supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model
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    Chapter 44 Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices with Applications to Neuroimaging
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    Chapter 45 Simultaneous Spatial-Temporal Decomposition of Connectome-Scale Brain Networks by Deep Sparse Recurrent Auto-Encoders
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    Chapter 46 Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention
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    Chapter 47 A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces
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    Chapter 48 A Geometric Framework for Feature Mappings in Multimodal Fusion of Brain Image Data
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    Chapter 49 A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data
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    Chapter 50 A Model for Elastic Evolution on Foliated Shapes
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    Chapter 51 Analyzing Mild Cognitive Impairment Progression via Multi-view Structural Learning
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    Chapter 52 New Graph-Blind Convolutional Network for Brain Connectome Data Analysis
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    Chapter 53 CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation
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    Chapter 54 Data-Driven Model Order Reduction for Diffeomorphic Image Registration
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    Chapter 55 DGR-Net: Deep Groupwise Registration of Multispectral Images
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    Chapter 56 Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
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    Chapter 57 Generalizations of Ripley’s K-function with Application to Space Curves
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    Chapter 58 Group Level MEG/EEG Source Imaging via Optimal Transport: Minimum Wasserstein Estimates
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    Chapter 59 InSpect: INtegrated SPECTral Component Estimation and Mapping for Multi-contrast Microstructural MRI
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    Chapter 60 Joint Inference on Structural and Diffusion MRI for Sequence-Adaptive Bayesian Segmentation of Thalamic Nuclei with Probabilistic Atlases
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    Chapter 61 Learning-Based Optimization of the Under-Sampling Pattern in MRI
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    Chapter 62 Melanoma Recognition via Visual Attention
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    Chapter 63 Nonlinear Markov Random Fields Learned via Backpropagation
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    Chapter 64 Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler
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    Chapter 65 SHAMANN: Shared Memory Augmented Neural Networks
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    Chapter 66 Signet Ring Cell Detection with a Semi-supervised Learning Framework
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    Chapter 67 Spherical U-Net on Cortical Surfaces: Methods and Applications
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    Chapter 68 Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis