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Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting [[electronic resource] ] : 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
| Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting [[electronic resource] ] : 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 |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XVII, 212 p. 83 illus., 68 illus. in color.) |
| Disciplina | 610.285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Image Processing and Computer Vision Artificial Intelligence |
| ISBN | 3-030-33327-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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. |
| Record Nr. | UNISA-996466194903316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting [[electronic resource] ] : 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
| Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting [[electronic resource] ] : 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 |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XVII, 212 p. 83 illus., 68 illus. in color.) |
| Disciplina | 610.285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Image Processing and Computer Vision Artificial Intelligence |
| ISBN | 3-030-33327-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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. |
| Record Nr. | UNINA-9910349274303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||