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 /: 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. (2019)

• Record Type: Book
• Further Information: Note: Hongen Liao, Simone Balocco, Guijin Wang [and eleven others].
• Editors: Liao, Hongen; Balocco, Simone; (Writer on iImage processing), Wang, Guijin
• Other Names: MLMECH (Workshop), 1st; CVII-STENT (Workshop), 8th; International Conference on Medical Image Computing and Computer-Assisted Intervention, 22nd
• Contents: Intro; Additional Workshop Editors; MLMECH-MICCAI 2019 Preface; Organization; Joint MICCAI-Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (MICCAI CVII-STENT 2019); Organization; Contents; Proceedings of the Machine Learning and Medical Engineering for Cardiovascular Health; Arrhythmia Classification with Attention-Based Res-BiLSTM-Net; Abstract; 1 Introduction; 2 Methods; 2.1 Attention-Based Resnet; 2.2 Attention-Based BiLSTM; 3 Experiment; 3.1 Dataset Description; 3.2 Experiment Setup; 3.3 Evaluation Metrics 3.4 Model Performance on Test Dataset4 Conclusion and Discussion; Acknowledgment; References; A Multi-label Learning Method to Detect Arrhythmia Based on 12-Lead ECGs; Abstract; 1 Introduction; 2 Dataset and the Proposed Method; 2.1 Database; 2.2 Data Preprocessing; 2.3 Two Losses; 2.4 SE-ResNet; 2.5 Test Protocol; 3 Experimental Results; 4 Conclusions; Acknowledgements; References; An Ensemble Neural Network for Multi-label Classification of Electrocardiogram; 1 Introduction; 2 Model Architecture; 2.1 Sequence Generation Module; 2.2 Multi-task Module; 2.3 Implementation Details; 3 Experiment 3.1 Dataset and Evaluation Metric3.2 Results; 4 Conclusion and Future Work; References; Automatic Diagnosis with 12-Lead ECG Signals; 1 Introduction; 2 Dataset Description; 3 Methods; 3.1 Data Pre-processing; 3.2 Feature Engineering; 3.3 Deep Learning Models; 3.4 Overall Framework; 3.5 Training; 4 Competition Results; 5 Conclusion; References; Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks; 1 Introduction; 2 Dataset; 3 The Architecture; 3.1 Heuristic Features; 3.2 Global Pooling Layer; 4 Details of Learning 4.1 Data Preprocessing4.2 Data Augmentation; 4.3 Optimization; 5 Results; 6 Conclusion; References; Transfer Learning for Electrocardiogram Classification Under Small Dataset; Abstract; 1 Introduction; 2 Methodology; 2.1 Deep Residual Network for Electrocardiogram Classification; 2.2 Network Training; 2.3 Evaluation Metric; 3 Dataset; 4 Results; 5 Conclusion; Acknowledgements; References; Multi-label Classification of Abnormalities in 12-Lead ECG Using 1D CNN and LSTM; Abstract; 1 Introduction; 2 Methods; 2.1 Data Description; 2.2 Architectures; 2.3 Training; 3 Results 4 Conclusion and DiscussionAcknowledgments; References; An Approach to Predict Multiple Cardiac Diseases; Abstract; 1 Introduction; 1.1 Background; 1.2 Project Introduction; 2 Methods; 2.1 QRS Detection and Median Complex; 2.2 Detection of Morphology Abnormalities; 2.3 Detection of Rhythm Abnormalities; 2.4 CNN Model; 2.5 Machine Learning Model; 3 Results; Acknowledgement; References; A 12-Lead ECG Arrhythmia Classification Method Based on 1D Densely Connected CNN; Abstract; 1 Introduction; 2 Methods; 2.1 Data Augmentation; 2.2 Data Segmentation; 2.3 Model Architecture and Training
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2019
• Extent: 1 online resource (xvii, 212 pages), illustrations (some color)
• Subjects: 610.285; Medical informatics -- Congresses; Biomedical engineering -- Congresses; Radiography, Medical -- Congresses; Electronic books; Electronic books
• Languages: English
• ISBNs: 9783030333270; 3030333272
• Related ISBNs: 9783030333263
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed October 16, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.