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Deep Learning and Data Labeling for Medical Applications

Overview of attention for book
Cover of 'Deep Learning and Data Labeling for Medical Applications'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNs
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    Chapter 2 Robust 3D Organ Localization with Dual Learning Architectures and Fusion
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    Chapter 3 Cell Segmentation Proposal Network for Microscopy Image Analysis
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    Chapter 4 Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks
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    Chapter 5 Convolutional Neural Network for Reconstruction of 7T-like Images from 3T MRI Using Appearance and Anatomical Features
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    Chapter 6 Fast Predictive Image Registration
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    Chapter 7 Longitudinal Multiple Sclerosis Lesion Segmentation Using Multi-view Convolutional Neural Networks
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    Chapter 8 Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks
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    Chapter 9 Fully Convolutional Network for Liver Segmentation and Lesions Detection
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    Chapter 10 Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis
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    Chapter 11 De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks
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    Chapter 12 Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting
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    Chapter 13 Medical Image Description Using Multi-task-loss CNN
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    Chapter 14 Fully Automating Graf’s Method for DDH Diagnosis Using Deep Convolutional Neural Networks
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    Chapter 15 Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data
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    Chapter 16 Learning Thermal Process Representations for Intraoperative Analysis of Cortical Perfusion During Ischemic Strokes
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    Chapter 17 Automatic Slice Identification in 3D Medical Images with a ConvNet Regressor
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    Chapter 18 Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks
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    Chapter 19 The Importance of Skip Connections in Biomedical Image Segmentation
  21. Altmetric Badge
    Chapter 20 Understanding the Mechanisms of Deep Transfer Learning for Medical Images
  22. Altmetric Badge
    Chapter 21 A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography
  23. Altmetric Badge
    Chapter 22 Early Experiences with Crowdsourcing Airway Annotations in Chest CT
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    Chapter 23 Hierarchical Feature Extraction for Nuclear Morphometry-Based Cancer Diagnosis
  25. Altmetric Badge
    Chapter 24 Using Crowdsourcing for Multi-label Biomedical Compound Figure Annotation
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    Chapter 25 Towards the Semantic Enrichment of Free-Text Annotation of Image Quality Assessment for UK Biobank Cardiac Cine MRI Scans
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    Chapter 26 Focused Proofreading to Reconstruct Neural Connectomes from EM Images at Scale
  28. Altmetric Badge
    Chapter 27 Hands-Free Segmentation of Medical Volumes via Binary Inputs
  29. Altmetric Badge
    Chapter 28 Playsourcing: A Novel Concept for Knowledge Creation in Biomedical Research
  30. Altmetric Badge
    Chapter 29 Erratum to: Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks
Attention for Chapter 22: Early Experiences with Crowdsourcing Airway Annotations in Chest CT
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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15 X users

Citations

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Readers on

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39 Mendeley
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Chapter title
Early Experiences with Crowdsourcing Airway Annotations in Chest CT
Chapter number 22
Book title
Deep Learning and Data Labeling for Medical Applications
Published in
Lecture notes in computer science, September 2016
DOI 10.1007/978-3-319-46976-8_22
Book ISBNs
978-3-31-946975-1, 978-3-31-946976-8, 978-3-31-946975-1, 978-3-31-946976-8
Authors

Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne, Cheplygina, Veronika, Perez-Rovira, Adria, Kuo, Wieying, Tiddens, Harm A. W. M., de Bruijne, Marleen

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 39 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Researcher 7 18%
Student > Master 6 15%
Student > Doctoral Student 3 8%
Other 2 5%
Other 4 10%
Unknown 8 21%
Readers by discipline Count As %
Computer Science 15 38%
Engineering 6 15%
Biochemistry, Genetics and Molecular Biology 1 3%
Agricultural and Biological Sciences 1 3%
Mathematics 1 3%
Other 2 5%
Unknown 13 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 28 April 2019.
All research outputs
#4,001,049
of 22,965,074 outputs
Outputs from Lecture notes in computer science
#938
of 8,137 outputs
Outputs of similar age
#66,992
of 323,305 outputs
Outputs of similar age from Lecture notes in computer science
#60
of 544 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,137 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 88% 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 323,305 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 544 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.