國立高雄大學圖資館 |

Language: English

Back

Machine learning and medical enginee...

(1998 :)

Machine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stentingfirst International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019 : proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stentingedited by Hongen Liao ... [et al.].
Reminder of title:	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 /
remainder title:	MLMECH 2019
other author:	Liao, Hongen.
corporate name:
Published:	Cham :Springer International Publishing :2019.
Description:	xvii, 212 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Medical informaticsCongresses.
Online resource:	https://doi.org/10.1007/978-3-030-33327-0
ISBN:	9783030333270$q(electronic bk.)

Machine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stentingfirst International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019 : proceedings /
Machine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stentingfirst International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019 : proceedings /[electronic resource] :MLMECH 2019edited by Hongen Liao ... [et al.]. - Cham :Springer International Publishing :2019. - xvii, 212 p. :ill. (some col.), digital ;24 cm. - Lecture notes in computer science,117940302-9743 ;. - Lecture notes in computer science ;4891..

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.

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.

ISBN: 9783030333270$q(electronic bk.)

Standard No.: 10.1007/978-3-030-33327-0doiSubjects--Topical Terms:

384533
Medical informatics
--Congresses.

LC Class. No.: R858.A2 / M55 2019

Dewey Class. No.: 610.285

Machine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stentingfirst International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019 : proceedings /
LDR:04600nmm a2200397 a 4500 001 567961
003 DE-He213
005 20191028213306.0
006 m d
007 cr nn 008maaau
008 200611s2019 sz s 0 eng d
020 $a 9783030333270$q(electronic bk.)
020 $a 9783030333263$q(paper)
024 7 $a 10.1007/978-3-030-33327-0 $2 doi
035 $a 978-3-030-33327-0
040 $a GP $c GP
041 0 $a eng
050 4 $a R858.A2 $b M55 2019
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 610.285 $2 23
090 $a R858.A2 $b M685 2019
111 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
245 1 0 $a Machine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stenting $h [electronic resource] : $b 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 / $c edited by Hongen Liao ... [et al.].
246 3 $a MLMECH 2019
246 3 $a CVII-STENT 2019
246 3 $a MICCAI 2019
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xvii, 212 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 11794
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a 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.
520 $a 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.
650 0 $a Medical informatics $v Congresses. $3 384533
650 0 $a Biomedical engineering $3 252564
650 0 $a Radiography, Medical $v Outlines, syllabi, etc. $3 397599
650 1 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Liao, Hongen. $3 285746
710 2 $a SpringerLink (Online service) $3 273601
711 2 $n (3rd : $d 1998 : $c Amsterdam, Netherlands) $3 194767
773 0 $t Springer eBooks
830 0 $a Lecture notes in computer science ; $v 4891. $3 383229
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 823073
856 4 0 $u https://doi.org/10.1007/978-3-030-33327-0
950 $a Computer Science (Springer-11645)