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Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting

Overview of attention for book
Cover of 'Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Arrhythmia Classification with Attention-Based Res-BiLSTM-Net
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    Chapter 2 A Multi-label Learning Method to Detect Arrhythmia Based on 12-Lead ECGs
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    Chapter 3 An Ensemble Neural Network for Multi-label Classification of Electrocardiogram
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    Chapter 4 Automatic Diagnosis with 12-Lead ECG Signals
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    Chapter 5 Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks
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    Chapter 6 Transfer Learning for Electrocardiogram Classification Under Small Dataset
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    Chapter 7 Multi-label Classification of Abnormalities in 12-Lead ECG Using 1D CNN and LSTM
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    Chapter 8 An Approach to Predict Multiple Cardiac Diseases
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    Chapter 9 A 12-Lead ECG Arrhythmia Classification Method Based on 1D Densely Connected CNN
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    Chapter 10 Automatic Multi-label Classification in 12-Lead ECGs Using Neural Networks and Characteristic Points
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    Chapter 11 Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention
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    Chapter 12 Deep Learning to Improve Heart Disease Risk Prediction
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    Chapter 13 LabelECG: A Web-Based Tool for Distributed Electrocardiogram Annotation
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    Chapter 14 Particle Swarm Optimization for Great Enhancement in Semi-supervised Retinal Vessel Segmentation with Generative Adversarial Networks
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    Chapter 15 Attention-Guided Decoder in Dilated Residual Network for Accurate Aortic Valve Segmentation in 3D CT Scans
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    Chapter 16 ARVBNet: Real-Time Detection of Anatomical Structures in Fetal Ultrasound Cardiac Four-Chamber Planes
  18. Altmetric Badge
    Chapter 17 The Effect of Labeling Duration and Temporal Resolution on Arterial Transit Time Estimation Accuracy in 4D ASL MRA Datasets - A Flow Phantom Study
  19. Altmetric Badge
    Chapter 18 Towards Quantifying Neurovascular Resilience
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    Chapter 19 Random 2.5D U-net for Fully 3D Segmentation
  21. Altmetric Badge
    Chapter 20 Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model
  22. Altmetric Badge
    Chapter 21 Tracking of Intracavitary Instrument Markers in Coronary Angiography Images
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    Chapter 22 Healthy Vessel Wall Detection Using U-Net in Optical Coherence Tomography
  24. Altmetric Badge
    Chapter 23 Advanced Multi-objective Design Analysis to Identify Ideal Stent Design
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    Chapter 24 Simultaneous Intracranial Artery Tracing and Segmentation from Magnetic Resonance Angiography by Joint Optimization from Multiplanar Reformation
Attention for Chapter 18: Towards Quantifying Neurovascular Resilience
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 X users

Citations

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6 Dimensions

Readers on

mendeley
9 Mendeley
Summary X Dimensions citations
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Chapter title
Towards Quantifying Neurovascular Resilience
Chapter number 18
Book title
Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting
Published in
arXiv, October 2019
DOI 10.1007/978-3-030-33327-0_18
Book ISBNs
978-3-03-033326-3, 978-3-03-033327-0
Authors

Stefano Moriconi, Rafael Rehwald, Maria A. Zuluaga, H. Rolf Jäger, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso, Moriconi, Stefano, Rehwald, Rafael, Zuluaga, Maria A., Jäger, H. Rolf, Nachev, Parashkev, Ourselin, Sébastien, Cardoso, M. Jorge

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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 22%
Lecturer > Senior Lecturer 1 11%
Professor 1 11%
Student > Bachelor 1 11%
Student > Master 1 11%
Other 1 11%
Unknown 2 22%
Readers by discipline Count As %
Engineering 2 22%
Computer Science 2 22%
Mathematics 1 11%
Medicine and Dentistry 1 11%
Economics, Econometrics and Finance 1 11%
Other 0 0%
Unknown 2 22%
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 02 November 2019.
All research outputs
#15,583,959
of 23,168,000 outputs
Outputs from arXiv
#379,641
of 953,740 outputs
Outputs of similar age
#217,711
of 353,809 outputs
Outputs of similar age from arXiv
#12,680
of 29,249 outputs
Altmetric has tracked 23,168,000 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 953,740 research outputs from this source. They receive a mean Attention Score of 3.9. 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 353,809 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29,249 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 50% of its contemporaries.

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