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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
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    Chapter 20 Understanding the Mechanisms of Deep Transfer Learning for Medical Images
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    Chapter 21 A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography
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    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
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    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
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    Chapter 27 Hands-Free Segmentation of Medical Volumes via Binary Inputs
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    Chapter 28 Playsourcing: A Novel Concept for Knowledge Creation in Biomedical Research
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    Chapter 29 Erratum to: Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks
Attention for Chapter 14: Fully Automating Graf’s Method for DDH Diagnosis Using Deep Convolutional Neural Networks
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Citations

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Chapter title
Fully Automating Graf’s Method for DDH Diagnosis Using Deep Convolutional Neural Networks
Chapter number 14
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_14
Book ISBNs
978-3-31-946975-1, 978-3-31-946976-8
Authors

David Golan, Yoni Donner, Chris Mansi, Jacob Jaremko, Manoj Ramachandran, on behalf of CUDL, on behalf of CUDL, Golan, David, Donner, Yoni, Mansi, Chris, Jaremko, Jacob, Ramachandran, Manoj

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 38 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 24%
Student > Ph. D. Student 9 24%
Researcher 5 13%
Student > Bachelor 4 11%
Student > Postgraduate 3 8%
Other 1 3%
Unknown 7 18%
Readers by discipline Count As %
Engineering 11 29%
Computer Science 10 26%
Medicine and Dentistry 4 11%
Arts and Humanities 2 5%
Agricultural and Biological Sciences 1 3%
Other 1 3%
Unknown 9 24%
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 28 January 2021.
All research outputs
#18,542,806
of 22,965,074 outputs
Outputs from Lecture notes in computer science
#6,023
of 8,137 outputs
Outputs of similar age
#245,788
of 323,305 outputs
Outputs of similar age from Lecture notes in computer science
#392
of 544 outputs
Altmetric has tracked 22,965,074 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,137 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 13th percentile – i.e., 13% 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 323,305 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
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 is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.

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