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

ISBN-13: 9783030333263 / Angielski / Miękka / 2019 / 212 str.

Hongen Liao; Simone Balocco; Guijin Wang

Spis treści

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.