menu
{ "item_title" : "Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting", "item_author" : [" Hongen Liao", "Simone Balocco", "Guijin Wang "], "item_description" : "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.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/03/033/326/3030333264_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting|Hongen Liao

Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting : First International Works

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

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 item is Non-Returnable

Details

  • ISBN-13: 9783030333263
  • ISBN-10: 3030333264
  • Publisher: Springer
  • Publish Date: October 2019
  • Dimensions: 9.21 x 6.14 x 0.49 inches
  • Shipping Weight: 0.73 pounds
  • Page Count: 212

Related Categories

You May Also Like...

    1

BAM Customer Reviews