Deep Learning and Data Labeling for Medical Applications

First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings

  • Gustavo Carneiro
  • Diana Mateus
  • Loïc Peter
  • Andrew Bradley
  • João Manuel R. S. Tavares
  • Vasileios Belagiannis
  • João Paulo Papa
  • Jacinto C. Nascimento
  • Marco Loog
  • Zhi Lu
  • Jaime S. Cardoso
  • Julien Cornebise
Conference proceedings DLMIA 2016, LABELS 2016

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10008)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 10008)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Deep Learning in Medical Image Analysis

    1. Front Matter
      Pages 1-1
    2. Xian-Hua Han, Jianmei Lei, Yen-Wei Chen
      Pages 3-11
    3. Xiaoguang Lu, Daguang Xu, David Liu
      Pages 12-20
    4. Saad Ullah Akram, Juho Kannala, Lauri Eklund, Janne Heikkilä
      Pages 21-29
    5. Xiao Yang, Roland Kwitt, Marc Niethammer
      Pages 48-57
    6. Daniel E. Worrall, Clare M. Wilson, Gabriel J. Brostow
      Pages 68-76
    7. Avi Ben-Cohen, Idit Diamant, Eyal Klang, Michal Amitai, Hayit Greenspan
      Pages 77-85
    8. Youngjin Yoo, Lisa W. Tang, Tom Brosch, David K. B. Li, Luanne Metz, Anthony Traboulsee et al.
      Pages 86-94
    9. Ariel Benou, Ronel Veksler, Alon Friedman, Tammy Riklin Raviv
      Pages 95-110
    10. Xiangrong Zhou, Takaaki Ito, Ryosuke Takayama, Song Wang, Takeshi Hara, Hiroshi Fujita
      Pages 111-120
    11. Pavel Kisilev, Eli Sason, Ella Barkan, Sharbell Hashoul
      Pages 121-129
    12. David Golan, Yoni Donner, Chris Mansi, Jacob Jaremko, Manoj Ramachandran, on behalf of CUDL
      Pages 130-141
    13. Simon Andermatt, Simon Pezold, Philippe Cattin
      Pages 142-151
    14. Nico Hoffmann, Edmund Koch, Gerald Steiner, Uwe Petersohn, Matthias Kirsch
      Pages 152-160
    15. Bob D. de Vos, Max A. Viergever, Pim A. de Jong, Ivana Išgum
      Pages 161-169
    16. Dong Nie, Xiaohuan Cao, Yaozong Gao, Li Wang, Dinggang Shen
      Pages 170-178
    17. Michal Drozdzal, Eugene Vorontsov, Gabriel Chartrand, Samuel Kadoury, Chris Pal
      Pages 179-187
    18. Hariharan Ravishankar, Prasad Sudhakar, Rahul Venkataramani, Sheshadri Thiruvenkadam, Pavan Annangi, Narayanan Babu et al.
      Pages 188-196
    19. Ayelet Akselrod-Ballin, Leonid Karlinsky, Sharon Alpert, Sharbell Hasoul, Rami Ben-Ari, Ella Barkan
      Pages 197-205
  3. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

    1. Front Matter
      Pages 207-207
    2. Veronika Cheplygina, Adria Perez-Rovira, Wieying Kuo, Harm A. W. M. Tiddens, Marleen de Bruijne
      Pages 209-218
    3. Chi Liu, Yue Huang, Ligong Han, John A. Ozolek, Gustavo K. Rohde
      Pages 219-227
    4. Alba Garcia Seco de Herrera, Roger Schaer, Sameer Antani, Henning Müller
      Pages 228-237
    5. Valentina Carapella, Ernesto Jiménez-Ruiz, Elena Lukaschuk, Nay Aung, Kenneth Fung, Jose Paiva et al.
      Pages 238-248
    6. Florian Dubost, Loic Peter, Christian Rupprecht, Benjamin Gutierrez Becker, Nassir Navab
      Pages 259-268
    7. Shadi Albarqouni, Stefan Matl, Maximilian Baust, Nassir Navab, Stefanie Demirci
      Pages 269-277
  4. Daniel E. Worrall, Clare M. Wilson, Gabriel J. Brostow
    Pages E1-E1
  5. Back Matter
    Pages 279-280

About these proceedings


This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.
The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.


active learning deep learning human-computer interaction label uncertainty medical image analysis anatomical structure segmentation cell detection clinical prediction computer aided diagnosis convolutional neural network crowdsourcing domain adaptation MRI multi-label annotation neurosurgery parameter approximation semantic description semi-supervised learning transfer learning machine learning

Editors and affiliations

  • Gustavo Carneiro
    • 1
  • Diana Mateus
    • 2
  • Loïc Peter
    • 3
  • Andrew Bradley
    • 4
  • João Manuel R. S. Tavares
    • 5
  • Vasileios Belagiannis
    • 6
  • João Paulo Papa
    • 7
  • Jacinto C. Nascimento
    • 8
  • Marco Loog
    • 9
  • Zhi Lu
    • 10
  • Jaime S. Cardoso
    • 11
  • Julien Cornebise
    • 12
  1. 1.University of AdelaideAdelaideAustralia
  2. 2.Technical University of MunichGarchingGermany
  3. 3.Technical University of MunichGarchingGermany
  4. 4.University of QueenslandSt LuciaAustralia
  5. 5.Universidade do PortoPortoPortugal
  6. 6.University of OxfordOxfordUnited Kingdom
  7. 7.Universidade Estadual PaulistaBauruBrazil
  8. 8.Instituto Superior TécnicoLisbonPortugal
  9. 9.Delft University of TechnologyDelftThe Netherlands
  10. 10.University of South AustraliaAdelaideAustralia
  11. 11.Universidade do PortoPortoPortugal
  12. 12.Google DeepMindLondonUnited Kingdom

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