Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting : First International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / edited by 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.

Format:

Book

Contributor:

Liao, Hongen, editor.

Balocco, Simone, editor.

Wang, Guijin, editor.

Zhang, Feng, editor.

Liu, Yongpan, editor.

Ding, Zijian, editor.

Yang, Li, editor.

Phellan, Renzo, editor.

Zahnd, Guillaume, editor.

Breininger, Katharina, editor.

Albarqouni, Shadi, editor.

Moriconi, Stefano, editor.

Lee, Su-Lin, editor.

Demirci, Stefanie, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11794.

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11794

Language:

English

Subjects (All):

Optical data processing.

Artificial intelligence.

Image Processing and Computer Vision.

Artificial Intelligence.

Local Subjects:

Image Processing and Computer Vision.

Artificial Intelligence.

Physical Description:

1 online resource (XVII, 212 pages) : 83 illustrations, 68 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the First International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For MLMECH 2019, 16 papers were accepted for publication from a total of 21 submissions. They focus on machine learning techniques and analyzing of ECG data in the diagnosis of heart diseases. CVII-STENT 2019 accepted all 8 submissiones for publication. They contain technological and scientific research concerning endovascular procedures. .

Contents:

Proceedings of the Machine Learning and Medical Engineering for Cardiovascular Health, MLMECH 2019

Arrhythmia Classification with Attention-Based ResBiLSTM-Net

A Multi-Label Learning Method to detect Arrhythmia Based on

An Ensemble Neural Network for Multi-label Classification of Electrocardiogram

Automatic Diagnosis with 12-lead ECG Signals

Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks

Transfer Learning for Electrocardiogram Classification under Small Dataset

Multi-label classification of abnormalities in 12-lead ECG using 1D CNN and LSTM

An Approach to Predict Multiple Cardiac Diseases

A 12-lead ECG Arrhythmia Classification Method Based on 1D Densely Connected CNN

Automatic Multi-label Classification in 12-lead ECGs Using Neural Networks and Characteristic Points

Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention

Deep Learning to Improve Heart Disease Risk Prediction

LabelECG: A Web-based Tool for Distributed Electrocardiogram Annotation

Particle Swarm Optimization for Great Enhancement in Semi-Supervised Retinal Vessel Segmentation with Generative Adversarial Networks

Attention-Guided Decoder in Dilated Residual Network for Accurate Aortic Valve Segmentation in 3D CT Scans

ARVBNet: Real-time Detection of Anatomical Structures in Fetal Ultrasound Cardiac Four-chamber Planes

Proceedings of the Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019

The Effect of Labeling Duration and Temporal Resolution on Arterial Transit Time Estimation Accuracy in 4D ASL MRA Datasets - a Flow Phantom Study

Towards Quantifying Neurovascular Resilience

Random 2.5D U-net for Fully 3D Segmentation

Abdominal aortic aneurysm segmentation using convolutional neural networks trained with images generated with a synthetic shape model

Tracking of intracavitary instrument markers in coronary angiography images

Healthy Vessel Wall Detection Using U-Net in Optical Coherence Tomography

Advanced Multi-objective Design Analysis to Identify Ideal Stent Design

Simultaneous Intracranial Artery Tracing and Segmentation from Magnetic Resonance Angiography by Joint Optimization from Multiplanar Reformation.

Other Format:

Printed edition:

ISBN:

978-3-030-33327-0

9783030333270

Access Restriction:

Restricted for use by site license.