Advertisement

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

  • 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
Conference proceedings DLMIA 2017, ML-CDS 2017

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

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 10553)

Table of contents

  1. Front Matter
    Pages I-XIX
  2. Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017

    1. Front Matter
      Pages 1-1
    2. Abhay Shah, Michael D. Abramoff, Xiaodong Wu
      Pages 3-11
    3. Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan
      Pages 21-29
    4. Ohad Shitrit, Tammy Riklin Raviv
      Pages 30-38
    5. Honghui Liu, Jianjiang Feng, Zishun Feng, Jiwen Lu, Jie Zhou
      Pages 39-46
    6. Siqi Bao, Pei Wang, Albert C. S. Chung
      Pages 47-55
    7. Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim
      Pages 56-64
    8. Shekoufeh Gorgi Zadeh, Maximilian W. M. Wintergerst, Vitalis Wiens, Sarah Thiele, Frank G. Holz, Robert P. Finger et al.
      Pages 65-73
    9. S. M. Masudur Rahman Al Arif, Karen Knapp, Greg Slabaugh
      Pages 74-82
    10. Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta
      Pages 83-91
    11. Fatemeh Taheri Dezaki, Neeraj Dhungel, Amir H. Abdi, Christina Luong, Teresa Tsang, John Jue et al.
      Pages 100-108
    12. Matthieu Le, Jesse Lieman-Sifry, Felix Lau, Sean Sall, Albert Hsiao, Daniel Golden
      Pages 109-116
    13. Yuru Pei, Yungeng Zhang, Haifang Qin, Gengyu Ma, Yuke Guo, Tianmin Xu et al.
      Pages 117-125
    14. Min Tang, Sepehr Valipour, Zichen Zhang, Dana Cobzas, Martin Jagersand
      Pages 126-134
    15. D. Bug, S. Schneider, A. Grote, E. Oswald, F. Feuerhake, J. Schüler et al.
      Pages 135-142
    16. Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne Martel
      Pages 143-150
    17. Behrouz Saghafi, Prabhat Garg, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Ananth J. Madhuranthakam et al.
      Pages 151-159
    18. William Lotter, Greg Sorensen, David Cox
      Pages 169-177
    19. Dheeraj Mundhra, Bharath Cheluvaraju, Jaiprasad Rampure, Tathagato Rai Dastidar
      Pages 178-185
    20. Xiaowei Hu, Lequan Yu, Hao Chen, Jing Qin, Pheng-Ann Heng
      Pages 186-194
    21. Kevin George, Adam P. Harrison, Dakai Jin, Ziyue Xu, Daniel J. Mollura
      Pages 195-203
    22. Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Marius Staring, Ivana Išgum
      Pages 204-212
    23. Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Ken’ichi Karasawa, Takayuki Kitasaka, Kazunari Misawa et al.
      Pages 222-230
    24. Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim
      Pages 231-239
    25. Carole H. Sudre, Wenqi Li, Tom Vercauteren, Sebastien Ourselin, M. Jorge Cardoso
      Pages 240-248
    26. Inwan Yoo, David G. C. Hildebrand, Willie F. Tobin, Wei-Chung Allen Lee, Won-Ki Jeong
      Pages 249-257
    27. Michal Sofka, Fausto Milletari, Jimmy Jia, Alex Rothberg
      Pages 258-266
    28. Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer et al.
      Pages 267-275
    29. Arijit Patra, Weilin Huang, J. Alison Noble
      Pages 276-284
    30. Aleksander Klibisz, Derek Rose, Matthew Eicholtz, Jay Blundon, Stanislav Zakharenko
      Pages 285-293
    31. Amir Jamaludin, Timor Kadir, Andrew Zisserman
      Pages 294-302
    32. Xinzi He, Zhen Yu, Tianfu Wang, Baiying Lei
      Pages 303-311
    33. Ayelet Akselrod-Ballin, Leonid. Karlinsky, Alon Hazan, Ran Bakalo, Ami Ben Horesh, Yoel Shoshan et al.
      Pages 321-329
    34. Adam Porisky, Tom Brosch, Emil Ljungberg, Lisa Y. W. Tang, Youngjin Yoo, Benjamin De Leener et al.
      Pages 330-337
  3. 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017

    1. Front Matter
      Pages 339-339
    2. Johannes Hofmanninger, Bjoern Menze, Marc-André Weber, Georg Langs
      Pages 341-348
    3. Ravnoor S. Gill, Seok-Jun Hong, Fatemeh Fadaie, Benoit Caldairou, Boris Bernhardt, Neda Bernasconi et al.
      Pages 349-356
    4. Manon Ansart, Stéphane Epelbaum, Geoffroy Gagliardi, Olivier Colliot, Didier Dormont, Bruno Dubois et al.
      Pages 357-364
    5. Shikha Chaganti, Jamie R. Robinson, Camilo Bermudez, Thomas Lasko, Louise A. Mawn, Bennett A. Landman
      Pages 373-381
  4. Zhipeng Ding, Greg Fleishman, Xiao Yang, Paul Thompson, Roland Kwitt, Marc Niethammer et al.
    Pages E1-E1
  5. Back Matter
    Pages 383-385

About these proceedings

Introduction

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.

Keywords

artificial intelligence classification classification accuracy computer architecture computer vision computerized tomography graph theory image analysis image processing image reconstruction image registration image segmentation learning algorithms learning systems mammography medical imaging neural networks segmentation methods Support Vector Machines (SVM)

Editors and affiliations

  • M. Jorge Cardoso
    • 1
  • Tal Arbel
    • 2
  • Gustavo Carneiro
    • 3
  • Tanveer Syeda-Mahmood
    • 4
  • João Manuel R.S. Tavares
    • 5
  • Mehdi Moradi
    • 6
  • Andrew Bradley
    • 7
  • Hayit Greenspan
    • 8
  • João Paulo Papa
    • 9
  • Anant Madabhushi
    • 10
  • Jacinto C. Nascimento
    • 11
  • Jaime S. Cardoso
    • 12
  • Vasileios Belagiannis
    • 13
  • Zhi Lu
    • 14
  1. 1.University College LondonLondonUnited Kingdom
  2. 2.McGill UniversityMontrealCanada
  3. 3.University of AdelaideAdelaideAustralia
  4. 4.IBM Research - AlmadenSan JoseUSA
  5. 5.Universidade do PortoPortoPortugal
  6. 6.IBM Research - AlmadenSan JoseUSA
  7. 7.University of QueenslandBrisbaneAustralia
  8. 8.Tel Aviv UniversityTel AvivIsrael
  9. 9.Universidade Estadual PaulistaBauruBrazil
  10. 10.Case Western Reserve UniversityClevelandUSA
  11. 11.Instituto Superior TécnicoLisboaPortugal
  12. 12.Universidade do PortoPortoPortugal
  13. 13.University of OxfordOxfordUnited Kingdom
  14. 14.University of South AustraliaAdelaideAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-67558-9
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-67557-2
  • Online ISBN 978-3-319-67558-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site