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Machine Learning in Medical Imaging

Overview of attention for book
Cover of 'Machine Learning in Medical Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph
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    Chapter 2 Bilateral Regularization in Reproducing Kernel Hilbert Spaces for Discontinuity Preserving Image Registration
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    Chapter 3 Do We Need Large Annotated Training Data for Detection Applications in Biomedical Imaging? A Case Study in Renal Glomeruli Detection
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    Chapter 4 Building an Ensemble of Complementary Segmentation Methods by Exploiting Probabilistic Estimates
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    Chapter 5 Learning Appearance and Shape Evolution for Infant Image Registration in the First Year of Life
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    Chapter 6 Detecting Osteophytes in Radiographs of the Knee to Diagnose Osteoarthritis
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    Chapter 7 Direct Estimation of Fiber Orientations Using Deep Learning in Diffusion Imaging
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    Chapter 8 Segmentation of Perivascular Spaces Using Vascular Features and Structured Random Forest from 7T MR Image
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    Chapter 9 Dual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images
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    Chapter 10 Joint Discriminative and Representative Feature Selection for Alzheimer’s Disease Diagnosis
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    Chapter 11 Patch-Based Hippocampus Segmentation Using a Local Subspace Learning Method
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    Chapter 12 Improving Single-Modal Neuroimaging Based Diagnosis of Brain Disorders via Boosted Privileged Information Learning Framework
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    Chapter 13 A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation
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    Chapter 14 Deep Ensemble Sparse Regression Network for Alzheimer’s Disease Diagnosis
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    Chapter 15 Learning Representation for Histopathological Image with Quaternion Grassmann Average Network
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    Chapter 16 Learning Global and Cluster-Specific Classifiers for Robust Brain Extraction in MR Data
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    Chapter 17 Cross-Modality Anatomical Landmark Detection Using Histograms of Unsigned Gradient Orientations and Atlas Location Autocontext
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    Chapter 18 Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Detection
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    Chapter 19 Segmentation-Free Estimation of Kidney Volumes in CT with Dual Regression Forests
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    Chapter 20 Multi-resolution-Tract CNN with Hybrid Pretrained and Skin-Lesion Trained Layers
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    Chapter 21 Retinal Image Quality Classification Using Saliency Maps and CNNs
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    Chapter 22 Unsupervised Discovery of Emphysema Subtypes in a Large Clinical Cohort
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    Chapter 23 Tree-Based Transforms for Privileged Learning
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    Chapter 24 Automated 3D Ultrasound Biometry Planes Extraction for First Trimester Fetal Assessment
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    Chapter 25 Learning for Graph-Based Sensorless Freehand 3D Ultrasound
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    Chapter 26 Learning-Based 3T Brain MRI Segmentation with Guidance from 7T MRI Labeling
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    Chapter 27 Transductive Maximum Margin Classification of ADHD Using Resting State fMRI
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    Chapter 28 Automatic Hippocampal Subfield Segmentation from 3T Multi-modality Images
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    Chapter 29 Regression Guided Deformable Models for Segmentation of Multiple Brain ROIs
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    Chapter 30 Functional Connectivity Network Fusion with Dynamic Thresholding for MCI Diagnosis
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    Chapter 31 Sparse Coding Based Skin Lesion Segmentation Using Dynamic Rule-Based Refinement
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    Chapter 32 Structure Fusion for Automatic Segmentation of Left Atrial Aneurysm Based on Deep Residual Networks
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    Chapter 33 Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface
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    Chapter 34 Iterative Dual LDA: A Novel Classification Algorithm for Resting State fMRI
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    Chapter 35 Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
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    Chapter 36 Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI
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    Chapter 37 Novel Morphological Features for Non-mass-like Breast Lesion Classification on DCE-MRI
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    Chapter 38 Fast Neuroimaging-Based Retrieval for Alzheimer’s Disease Analysis
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#4 of 8,165)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
63 news outlets
blogs
1 blog
twitter
31 X users

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
2 Mendeley
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Title
Machine Learning in Medical Imaging
Published by
Lecture notes in computer science, January 2016
DOI 10.1007/978-3-319-47157-0
ISBNs
978-3-31-947156-3, 978-3-31-947157-0
Editors

Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk

X Demographics

X Demographics

The data shown below were collected from the profiles of 31 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 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 1 50%
Unknown 1 50%
Readers by discipline Count As %
Engineering 1 50%
Unknown 1 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 514. 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 24 April 2017.
All research outputs
#49,735
of 25,522,520 outputs
Outputs from Lecture notes in computer science
#4
of 8,165 outputs
Outputs of similar age
#689
of 400,517 outputs
Outputs of similar age from Lecture notes in computer science
#4
of 582 outputs
Altmetric has tracked 25,522,520 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,165 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 99% 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 400,517 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 582 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.