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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
  2. Altmetric Badge
    Chapter 1 Robust Fréchet Mean and PGA on Riemannian Manifolds with Applications to Neuroimaging
  3. Altmetric Badge
    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
  32. Altmetric Badge
    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
  34. Altmetric Badge
    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
  36. Altmetric Badge
    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
  38. Altmetric Badge
    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
  41. Altmetric Badge
    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
  45. Altmetric Badge
    Chapter 44 Frequency Diffeomorphisms for Efficient Image Registration
  46. Altmetric Badge
    Chapter 45 A Stochastic Large Deformation Model for Computational Anatomy
  47. Altmetric Badge
    Chapter 46 Symmetric Interleaved Geodesic Shooting in Diffeomorphisms
  48. Altmetric Badge
    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
  50. Altmetric Badge
    Chapter 49 Joint Deep Learning of Foreground, Background and Shape for Robust Contextual Segmentation
  51. Altmetric Badge
    Chapter 50 Automatic Vertebra Labeling in Large-Scale 3D CT Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
  52. Altmetric Badge
    Chapter 51 A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
  53. Altmetric Badge
    Chapter 52 Population Based Image Imputation
  54. Altmetric Badge
    Chapter 53 VTrails: Inferring Vessels with Geodesic Connectivity Trees
Attention for Chapter 14: A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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Chapter title
A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies
Chapter number 14
Book title
Information Processing in Medical Imaging
Published in
Information processing in medical imaging proceedings of the conference, June 2017
DOI 10.1007/978-3-319-59050-9_14
Pubmed ID
Book ISBNs
978-3-31-959049-3, 978-3-31-959050-9
Authors

Jenna Schabdach, William M. WellsIII, Michael Cho, Kayhan N. Batmanghelich, William M. Wells

Abstract

We propose a non-parametric approach for characterizing heterogeneous diseases in large-scale studies. We target diseases where multiple types of pathology present simultaneously in each subject and a more severe disease manifests as a higher level of tissue destruction. For each subject, we model the collection of local image descriptors as samples generated by an unknown subject-specific probability density. Instead of approximating the probability density via a parametric family, we propose to side step the parametric inference by directly estimating the divergence between subject densities. Our method maps the collection of local image descriptors to a signature vector that is used to predict a clinical measurement. We are able to interpret the prediction of the clinical variable in the population and individual levels by carefully studying the divergences. We illustrate an application this method on simulated data as well as on a large-scale lung CT study of Chronic Obstructive Pulmonary Disease (COPD). Our approach outperforms classical methods on both simulated and COPD data and demonstrates the state-of-the-art prediction on an important physiologic measure of airflow (the forced respiratory volume in one second, FEV1).

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 4 21%
Student > Ph. D. Student 3 16%
Researcher 3 16%
Student > Master 3 16%
Student > Postgraduate 2 11%
Other 2 11%
Unknown 2 11%
Readers by discipline Count As %
Computer Science 5 26%
Medicine and Dentistry 5 26%
Engineering 4 21%
Nursing and Health Professions 1 5%
Agricultural and Biological Sciences 1 5%
Other 0 0%
Unknown 3 16%
Attention Score in Context

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 22 June 2017.
All research outputs
#7,217,186
of 22,981,247 outputs
Outputs from Information processing in medical imaging proceedings of the conference
#9
of 40 outputs
Outputs of similar age
#114,701
of 315,590 outputs
Outputs of similar age from Information processing in medical imaging proceedings of the conference
#1
of 9 outputs
Altmetric has tracked 22,981,247 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 40 research outputs from this source. They receive a mean Attention Score of 2.7. This one scored the same or higher as 31 of them.
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 315,590 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 63% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them