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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. M. Jorge Cardoso

    1. University College London, London, United Kingdom

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  2. Tal Arbel

    1. McGill University, Montreal, Canada

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  3. Gustavo Carneiro

    1. University of Adelaide, Adelaide, Australia

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  4. Tanveer Syeda-Mahmood

    1. IBM Research - Almaden, San Jose, USA

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  5. João Manuel R.S. Tavares

    1. Universidade do Porto, Porto, Portugal

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  6. Mehdi Moradi

    1. IBM Research - Almaden, San Jose, USA

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  7. Andrew Bradley

    1. University of Queensland, Brisbane, Australia

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  8. Hayit Greenspan

    1. Tel Aviv University, Tel Aviv, Israel

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  9. João Paulo Papa

    1. Universidade Estadual Paulista, Bauru, Brazil

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  10. Anant Madabhushi

    1. Case Western Reserve University, Cleveland, USA

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  11. Jacinto C. Nascimento

    1. Instituto Superior Técnico, Lisboa, Portugal

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  12. Jaime S. Cardoso

    1. Universidade do Porto, Porto, Portugal

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  13. Vasileios Belagiannis

    1. University of Oxford, Oxford, United Kingdom

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  14. Zhi Lu

    1. University of South Australia, Adelaide, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10553)

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

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Table of contents (44 papers)

  1. Front Matter

    Pages I-XIX
    Download chapter PDF

  2. Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Simultaneous Multiple Surface Segmentation Using Deep Learning

      • Abhay Shah, Michael D. Abramoff, Xiaodong Wu
      Pages 3-11

    3. A Deep Residual Inception Network for HEp-2 Cell Classification

      • Yuexiang Li, Linlin Shen
      Pages 12-20

    4. Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures

      • Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan
      Pages 21-29

    5. Accelerated Magnetic Resonance Imaging by Adversarial Neural Network

      • Ohad Shitrit, Tammy Riklin Raviv
      Pages 30-38

    6. Left Atrium Segmentation in CT Volumes with Fully Convolutional Networks

      • Honghui Liu, Jianjiang Feng, Zishun Feng, Jiwen Lu, Jie Zhou
      Pages 39-46

    7. 3D Randomized Connection Network with Graph-Based Inference

      • Siqi Bao, Pei Wang, Albert C. S. Chung
      Pages 47-55

    8. Adversarial Training and Dilated Convolutions for Brain MRI Segmentation

      • Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim
      Pages 56-64

    9. CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography

      • Shekoufeh Gorgi Zadeh, Maximilian W. M. Wintergerst, Vitalis Wiens, Sarah Thiele, Frank G. Holz, Robert P. Finger et al.
      Pages 65-73

    10. Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images

      • S. M. Masudur Rahman Al Arif, Karen Knapp, Greg Slabaugh
      Pages 74-82

    11. Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images

      • Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta
      Pages 83-91

    12. Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks

      • Sangheum Hwang, Sunggyun Park
      Pages 92-99

    13. Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms

      • Fatemeh Taheri Dezaki, Neeraj Dhungel, Amir H. Abdi, Christina Luong, Teresa Tsang, John Jue et al.
      Pages 100-108

    14. Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networks

      • Matthieu Le, Jesse Lieman-Sifry, Felix Lau, Sean Sall, Albert Hsiao, Daniel Golden
      Pages 109-116

    15. Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression

      • Yuru Pei, Yungeng Zhang, Haifang Qin, Gengyu Ma, Yuke Guo, Tianmin Xu et al.
      Pages 117-125

    16. A Deep Level Set Method for Image Segmentation

      • Min Tang, Sepehr Valipour, Zichen Zhang, Dana Cobzas, Martin Jagersand
      Pages 126-134

    17. Context-Based Normalization of Histological Stains Using Deep Convolutional Features

      • D. Bug, S. Schneider, A. Grote, E. Oswald, F. Feuerhake, J. Schüler et al.
      Pages 135-142

    18. Transitioning Between Convolutional and Fully Connected Layers in Neural Networks

      • Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne Martel
      Pages 143-150

    19. Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks

      • Behrouz Saghafi, Prabhat Garg, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Ananth J. Madhuranthakam et al.
      Pages 151-159
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Other volumes

  1. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Keywords

About this book

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017.

The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Editors and Affiliations

  • University College London, London, United Kingdom

    M. Jorge Cardoso

  • McGill University, Montreal, Canada

    Tal Arbel

  • University of Adelaide, Adelaide, Australia

    Gustavo Carneiro

  • IBM Research - Almaden, San Jose, USA

    Tanveer Syeda-Mahmood, Mehdi Moradi

  • Universidade do Porto, Porto, Portugal

    João Manuel R.S. Tavares, Jaime S. Cardoso

  • University of Queensland, Brisbane, Australia

    Andrew Bradley

  • Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  • Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  • Case Western Reserve University, Cleveland, USA

    Anant Madabhushi

  • Instituto Superior Técnico, Lisboa, Portugal

    Jacinto C. Nascimento

  • University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  • University of South Australia, Adelaide, Australia

    Zhi Lu

Bibliographic Information

  • Book Title: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

  • Book Subtitle: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings

  • Editors: M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, João Manuel R.S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, … Zhi Lu

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-319-67558-9

  • Publisher: Springer Cham

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

  • Copyright Information: Springer International Publishing AG 2017

  • Softcover ISBN: 978-3-319-67557-2Published: 09 September 2017

  • eBook ISBN: 978-3-319-67558-9Published: 07 September 2017

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIX, 385

  • Number of Illustrations: 169 b/w illustrations

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Health Informatics, Computational Biology/Bioinformatics, Logic Design

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