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Patch-Based Techniques in Medical Imaging

Overview of attention for book
Cover of 'Patch-Based Techniques in Medical Imaging'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 4D Multi-atlas Label Fusion Using Longitudinal Images
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    Chapter 2 Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks
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    Chapter 3 Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients
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    Chapter 4 Hippocampus Subfield Segmentation Using a Patch-Based Boosted Ensemble of Autocontext Neural Networks
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    Chapter 5 On the Role of Patch Spaces in Patch-Based Label Fusion
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    Chapter 6 Learning a Sparse Database for Patch-Based Medical Image Segmentation
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    Chapter 7 Accurate and High Throughput Cell Segmentation Method for Mouse Brain Nuclei Using Cascaded Convolutional Neural Network
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    Chapter 8 Learning-Based Estimation of Functional Correlation Tensors in White Matter for Early Diagnosis of Mild Cognitive Impairment
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    Chapter 9 Early Prediction of Alzheimer’s Disease with Non-local Patch-Based Longitudinal Descriptors
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    Chapter 10 Adaptive Fusion of Texture-Based Grading: Application to Alzheimer’s Disease Detection
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    Chapter 11 Micro-CT Guided 3D Reconstruction of Histological Images
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    Chapter 12 A Neural Regression Framework for Low-Dose Coronary CT Angiography (CCTA) Denoising
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    Chapter 13 A Dictionary Learning-Based Fast Imaging Method for Ultrasound Elastography
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    Chapter 14 Breast Tumor Detection in Ultrasound Images Using Deep Learning
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    Chapter 15 Modeling the Intra-class Variability for Liver Lesion Detection Using a Multi-class Patch-Based CNN
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    Chapter 16 Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion
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    Chapter 17 Deep Multimodal Case–Based Retrieval for Large Histopathology Datasets
  19. Altmetric Badge
    Chapter 18 Sparse Representation Using Block Decomposition for Characterization of Imaging Patterns
Attention for Chapter 6: Learning a Sparse Database for Patch-Based Medical Image Segmentation
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Citations

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Chapter title
Learning a Sparse Database for Patch-Based Medical Image Segmentation
Chapter number 6
Book title
Patch-Based Techniques in Medical Imaging
Published in
arXiv, September 2017
DOI 10.1007/978-3-319-67434-6_6
Book ISBNs
978-3-31-967433-9, 978-3-31-967434-6
Authors

Moti Freiman, Hannes Nickisch, Holger Schmitt, Pal Maurovich-Horvat, Patrick Donnelly, Mani Vembar, Liran Goshen

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer > Senior Lecturer 2 20%
Student > Ph. D. Student 2 20%
Researcher 2 20%
Student > Master 2 20%
Librarian 1 10%
Other 0 0%
Unknown 1 10%
Readers by discipline Count As %
Computer Science 6 60%
Medicine and Dentistry 1 10%
Engineering 1 10%
Unknown 2 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 26 June 2019.
All research outputs
#19,611,910
of 24,980,180 outputs
Outputs from arXiv
#470,474
of 1,018,032 outputs
Outputs of similar age
#235,153
of 321,545 outputs
Outputs of similar age from arXiv
#12,725
of 18,078 outputs
Altmetric has tracked 24,980,180 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,018,032 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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 321,545 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18,078 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.

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