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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

  • Conference proceedings
  • © 2019

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Editors:
  1. Hongen Liao

    1. Tsinghua University, Beijing, China

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  2. Simone Balocco

    1. University of Barcelona, Barcelona, Spain

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  3. Guijin Wang

    1. Tsinghua University, Beijing, China

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  4. Feng Zhang

    1. Chinese Academy of Sciences, Beijing, China

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  5. Yongpan Liu

    1. Tsinghua University, Beijing, China

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  6. Zijian Ding

    1. Tsinghua University, Beijing, China

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  7. Luc Duong

    1. École de Technologie Supérieure, Montreal, Canada

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  8. Renzo Phellan

    1. University of Calgary, Calgary, Canada

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  9. Guillaume Zahnd

    1. Technical University of Munich, Munich, Germany

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  10. Katharina Breininger

    1. Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany

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  11. Shadi Albarqouni

    1. Technical University of Munich, Munich, Germany

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  12. Stefano Moriconi

    1. University College London, London, UK

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  13. Su-Lin Lee

    1. Imperial College London, London, UK

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  14. Stefanie Demirci

    1. Technical University of Munich, Munich, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11794)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

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Table of contents (24 papers)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. Proceedings of the Machine Learning and Medical Engineering for Cardiovascular Health

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Arrhythmia Classification with Attention-Based Res-BiLSTM-Net

      • Chengbin Huang, Renjie Zhao, Weiting Chen, Huazheng Li
      Pages 3-10

    3. A Multi-label Learning Method to Detect Arrhythmia Based on 12-Lead ECGs

      • Jinjing Zhu, Kaifa Xin, Qingqing Zhao, Yue Zhang
      Pages 11-19

    4. An Ensemble Neural Network for Multi-label Classification of Electrocardiogram

      • Dongya Jia, Wei Zhao, Zhenqi Li, Cong Yan, Hongmei Wang, Jing Hu et al.
      Pages 20-27

    5. Automatic Diagnosis with 12-Lead ECG Signals

      • Ke Wang, Xuan Zhang, Haoxi Zhong, Ting Chen
      Pages 28-35

    6. Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks

      • Binhang Yuan, Wenhui Xing
      Pages 36-44

    7. Transfer Learning for Electrocardiogram Classification Under Small Dataset

      • Longting Chen, Guanghua Xu, Sicong Zhang, Jiachen Kuang, Long Hao
      Pages 45-54

    8. Multi-label Classification of Abnormalities in 12-Lead ECG Using 1D CNN and LSTM

      • Chengsi Luo, Hongxiu Jiang, Quanchi Li, Nini Rao
      Pages 55-63

    9. An Approach to Predict Multiple Cardiac Diseases

      • Guanghong Bin, Yongyue Sun, Jiao Huang, Guangyu Bin
      Pages 64-71

    10. A 12-Lead ECG Arrhythmia Classification Method Based on 1D Densely Connected CNN

      • Chunli Wang, Shan Yang, Xun Tang, Bin Li
      Pages 72-79

    11. Automatic Multi-label Classification in 12-Lead ECGs Using Neural Networks and Characteristic Points

      • Zhourui Xia, Zhenhua Sang, Yutong Guo, Weijie Ji, Chenguang Han, Yanlin Chen et al.
      Pages 80-87

    12. Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention

      • Yang Liu, Runnan He, Kuanquan Wang, Qince Li, Qiang Sun, Na Zhao et al.
      Pages 88-95

    13. Deep Learning to Improve Heart Disease Risk Prediction

      • Shelda Sajeev, Anthony Maeder, Stephanie Champion, Alline Beleigoli, Cheng Ton, Xianglong Kong et al.
      Pages 96-103

    14. LabelECG: A Web-Based Tool for Distributed Electrocardiogram Annotation

      • Zijian Ding, Shan Qiu, Yutong Guo, Jianping Lin, Li Sun, Dapeng Fu et al.
      Pages 104-111

    15. Particle Swarm Optimization for Great Enhancement in Semi-supervised Retinal Vessel Segmentation with Generative Adversarial Networks

      • Qiang Huo, Geyu Tang, Feng Zhang
      Pages 112-120

    16. Attention-Guided Decoder in Dilated Residual Network for Accurate Aortic Valve Segmentation in 3D CT Scans

      • Bowen Fan, Naoki Tomii, Hiroyuki Tsukihara, Eriko Maeda, Haruo Yamauchi, Kan Nawata et al.
      Pages 121-129

    17. ARVBNet: Real-Time Detection of Anatomical Structures in Fetal Ultrasound Cardiac Four-Chamber Planes

      • Jinbao Dong, Shengfeng Liu, Tianfu Wang
      Pages 130-137

  3. Proceedings of the Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting

    1. Front Matter

      Pages 139-139
      Download chapter PDF

    2. The Effect of Labeling Duration and Temporal Resolution on Arterial Transit Time Estimation Accuracy in 4D ASL MRA Datasets - A Flow Phantom Study

      • Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Nils D. Forkert
      Pages 141-148
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  1. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting

Keywords

About this book

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. 

Editors and Affiliations

  • Tsinghua University, Beijing, China

    Hongen Liao, Guijin Wang, Yongpan Liu, Zijian Ding

  • University of Barcelona, Barcelona, Spain

    Simone Balocco

  • Chinese Academy of Sciences, Beijing, China

    Feng Zhang

  • École de Technologie Supérieure, Montreal, Canada

    Luc Duong

  • University of Calgary, Calgary, Canada

    Renzo Phellan

  • Technical University of Munich, Munich, Germany

    Guillaume Zahnd, Shadi Albarqouni, Stefanie Demirci

  • Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany

    Katharina Breininger

  • University College London, London, UK

    Stefano Moriconi

  • Imperial College London, London, UK

    Su-Lin Lee

Bibliographic Information

  • Book Title: Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting

  • Book Subtitle: 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

  • Editors: 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

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-33327-0

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-33326-3Published: 13 October 2019

  • eBook ISBN: 978-3-030-33327-0Published: 12 October 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVII, 212

  • Number of Illustrations: 15 b/w illustrations, 68 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence

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