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

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

Table of Contents

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    Book Overview
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    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
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    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
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    Chapter 18 Towards Quantifying Neurovascular Resilience
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    Chapter 19 Random 2.5D U-net for Fully 3D Segmentation
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    Chapter 20 Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model
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    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
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    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
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Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-33327-0
978-3-03-033326-3, 978-3-03-033327-0

Hongen Liao, Simone Balocco, Guijin Wang, Feng Zhang, Yongpan Liu, Zijian Ding, Luc Duong, Renzo Phellan, Guillaume Zahnd, Katharina Breininger, Shadi Albarqouni, Stefano Moriconi, Su-Lin Lee, Stefanie Demirci

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 35%
Student > Master 5 15%
Student > Postgraduate 2 6%
Researcher 2 6%
Student > Bachelor 1 3%
Other 1 3%
Unknown 11 32%
Readers by discipline Count As %
Engineering 9 26%
Computer Science 8 24%
Agricultural and Biological Sciences 2 6%
Medicine and Dentistry 2 6%
Mathematics 2 6%
Other 1 3%
Unknown 10 29%