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Information Processing in Medical Imaging

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

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Robust Fréchet Mean and PGA on Riemannian Manifolds with Applications to Neuroimaging
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    Chapter 2 Inconsistency of Template Estimation with the Fréchet Mean in Quotient Space
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    Chapter 3 Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex
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    Chapter 4 Conditional Local Distance Correlation for Manifold-Valued Data
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    Chapter 5 Stochastic Development Regression on Non-linear Manifolds
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    Chapter 6 Spectral Kernels for Probabilistic Analysis and Clustering of Shapes
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    Chapter 7 Optimal Topological Cycles and Their Application in Cardiac Trabeculae Restoration
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    Chapter 8 From Label Maps to Generative Shape Models: A Variational Bayesian Learning Approach
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    Chapter 9 Constructing Shape Spaces from a Topological Perspective
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    Chapter 10 A Discriminative Event Based Model for Alzheimer’s Disease Progression Modeling
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    Chapter 11 A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images
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    Chapter 12 Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
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    Chapter 13 A Novel Dynamic Hyper-graph Inference Framework for Computer Assisted Diagnosis of Neuro-Diseases
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    Chapter 14 A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies
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    Chapter 15 Multi-source Multi-target Dictionary Learning for Prediction of Cognitive Decline
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    Chapter 16 Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-learned Structured Low-Rank Model
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    Chapter 17 Weakly-Supervised Evidence Pinpointing and Description
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    Chapter 18 Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models
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    Chapter 19 Cancer Metastasis Detection via Spatially Structured Deep Network
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    Chapter 20 Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
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    Chapter 21 Topographic Regularity for Tract Filtering in Brain Connectivity
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    Chapter 22 Riccati-Regularized Precision Matrices for Neuroimaging
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    Chapter 23 Multimodal Brain Subnetwork Extraction Using Provincial Hub Guided Random Walks
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    Chapter 24 Exact Topological Inference for Paired Brain Networks via Persistent Homology
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    Chapter 25 Multivariate Manifold Modelling of Functional Connectivity in Developing Language Networks
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    Chapter 26 Hierarchical Region-Network Sparsity for High-Dimensional Inference in Brain Imaging
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    Chapter 27 A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
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    Chapter 28 On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
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    Chapter 29 Discovering Change-Point Patterns in Dynamic Functional Brain Connectivity of a Population
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    Chapter 30 Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches
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    Chapter 31 Estimation of Brain Network Atlases Using Diffusive-Shrinking Graphs: Application to Developing Brains
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    Chapter 32 A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity
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    Chapter 33 Modeling Task fMRI Data via Deep Convolutional Autoencoder
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    Chapter 34 Director Field Analysis to Explore Local White Matter Geometric Structure in Diffusion MRI
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    Chapter 35 Decoupling Axial and Radial Tissue Heterogeneity in Diffusion Compartment Imaging
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    Chapter 36 Bayesian Dictionary Learning and Undersampled Multishell HARDI Reconstruction
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    Chapter 37 Estimation of Tissue Microstructure Using a Deep Network Inspired by a Sparse Reconstruction Framework
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    Chapter 38 HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics
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    Chapter 39 Orthotropic Thin Shell Elasticity Estimation for Surface Registration
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    Chapter 40 Direct Estimation of Regional Wall Thicknesses via Residual Recurrent Neural Network
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    Chapter 41 Multi-class Image Segmentation in Fluorescence Microscopy Using Polytrees
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    Chapter 42 Direct Estimation of Spinal Cobb Angles by Structured Multi-output Regression
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    Chapter 43 Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via a Novel Structured SCCA Approach
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    Chapter 44 Frequency Diffeomorphisms for Efficient Image Registration
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    Chapter 45 A Stochastic Large Deformation Model for Computational Anatomy
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    Chapter 46 Symmetric Interleaved Geodesic Shooting in Diffeomorphisms
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    Chapter 47 Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks
  49. Altmetric Badge
    Chapter 48 Globally Optimal Coupled Surfaces for Semi-automatic Segmentation of Medical Images
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    Chapter 49 Joint Deep Learning of Foreground, Background and Shape for Robust Contextual Segmentation
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    Chapter 50 Automatic Vertebra Labeling in Large-Scale 3D CT Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
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    Chapter 51 A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
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    Chapter 52 Population Based Image Imputation
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    Chapter 53 VTrails: Inferring Vessels with Geodesic Connectivity Trees
Attention for Chapter 28: On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

11 tweeters
2 Facebook pages


9 Dimensions

Readers on

223 Mendeley
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Chapter title
On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task
Chapter number 28
Book title
Information Processing in Medical Imaging
Published in
arXiv, June 2017
DOI 10.1007/978-3-319-59050-9_28
Book ISBNs
978-3-31-959049-3, 978-3-31-959050-9

Wenqi Li, Guotai Wang, Lucas Fidon, Sebastien Ourselin, M. Jorge Cardoso, Tom Vercauteren, Li, W, Wang, G, Fidon, L, Ourselin, S, Cardoso, MJ, Vercauteren, T

Twitter Demographics

The data shown below were collected from the profiles of 11 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 223 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 223 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 56 25%
Researcher 40 18%
Student > Master 35 16%
Student > Bachelor 15 7%
Student > Doctoral Student 13 6%
Other 28 13%
Unknown 36 16%
Readers by discipline Count As %
Computer Science 80 36%
Engineering 47 21%
Medicine and Dentistry 15 7%
Physics and Astronomy 7 3%
Neuroscience 4 2%
Other 14 6%
Unknown 56 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 June 2020.
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Altmetric has tracked 16,395,544 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 654,060 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,917 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 19,493 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.