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Information Processing in Medical Imaging

Overview of attention for book
Cover of 'Information Processing in Medical Imaging'

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
Overall attention for this book and its chapters
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Mentioned by

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1 news outlet
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72 tweeters
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1 Wikipedia page

Citations

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3 Dimensions

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Title
Information Processing in Medical Imaging
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-20351-1
ISBNs
978-3-03-020350-4, 978-3-03-020351-1
Editors

Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao

Twitter Demographics

The data shown below were collected from the profiles of 72 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 20%
Student > Bachelor 4 16%
Professor > Associate Professor 2 8%
Student > Doctoral Student 2 8%
Student > Master 2 8%
Other 3 12%
Unknown 7 28%
Readers by discipline Count As %
Computer Science 5 20%
Engineering 4 16%
Medicine and Dentistry 2 8%
Chemistry 1 4%
Psychology 1 4%
Other 2 8%
Unknown 10 40%