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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
  30. Altmetric Badge
    Chapter 29 Erratum to: Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks
Attention for Chapter 5: Convolutional Neural Network for Reconstruction of 7T-like Images from 3T MRI Using Appearance and Anatomical Features
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

patent
1 patent

Citations

dimensions_citation
70 Dimensions

Readers on

mendeley
81 Mendeley
Summary Patents Dimensions citations
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Chapter title
Convolutional Neural Network for Reconstruction of 7T-like Images from 3T MRI Using Appearance and Anatomical Features
Chapter number 5
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_5
Book ISBNs
978-3-31-946975-1, 978-3-31-946976-8
Authors

Khosro Bahrami, Feng Shi, Islem Rekik, Dinggang Shen, Bahrami, Khosro, Shi, Feng, Rekik, Islem, Shen, Dinggang

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 81 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 21%
Student > Ph. D. Student 16 20%
Student > Master 12 15%
Student > Bachelor 7 9%
Lecturer 3 4%
Other 12 15%
Unknown 14 17%
Readers by discipline Count As %
Computer Science 26 32%
Engineering 15 19%
Medicine and Dentistry 5 6%
Physics and Astronomy 5 6%
Agricultural and Biological Sciences 2 2%
Other 5 6%
Unknown 23 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 25 January 2024.
All research outputs
#8,538,940
of 25,385,509 outputs
Outputs from Lecture notes in computer science
#2,540
of 8,161 outputs
Outputs of similar age
#121,261
of 330,888 outputs
Outputs of similar age from Lecture notes in computer science
#174
of 552 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,161 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 gotten more attention than average, scoring higher than 53% 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 330,888 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 552 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.

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