© 2011

Machine Learning in Medical Imaging

Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings

  • Kenji Suzuki
  • Fei Wang
  • Dinggang Shen
  • Pingkun Yan
  • State-of-the-art research

  • Fast-track conference proceedings

  • Unique visibility

Conference proceedings MLMI 2011

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Table of contents

  1. Front Matter
  2. Peter Mysling, Kersten Petersen, Mads Nielsen, Martin Lillholm
    Pages 10-17
  3. Agnès Masson-Sibut, Amir Nakib, Eric Petit, François Leitner
    Pages 18-25
  4. Bahbibi Rahmatullah, Aris Papageorghiou, J. Alison Noble
    Pages 35-42
  5. Carsten Last, Simon Winkelbach, Friedrich M. Wahl, Klaus W. G. Eichhorn, Friedrich Bootz
    Pages 51-58
  6. Wei Liu, Suyash P. Awate, Jeffrey S. Anderson, Deborah Yurgelun-Todd, P. Thomas Fletcher
    Pages 59-66
  7. Gabriele Chiusano, Alessandra Staglianò, Curzio Basso, Alessandro Verri
    Pages 67-74
  8. Johannes Feulner, S. Kevin Zhou, Matthias Hammon, Joachim Hornegger, Dorin Comaniciu
    Pages 91-99
  9. Minjeong Kim, Guorong Wu, Wei Li, Li Wang, Young-Don Son, Zang-Hee Cho et al.
    Pages 100-108
  10. Uyen T. V. Nguyen, Alauddin Bhuiyan, Kotagiri Ramamohanarao, Laurence A. F. Park
    Pages 117-125
  11. Jiayin Zhou, Qi Tian, Vincent Chong, Wei Xiong, Weimin Huang, Zhimin Wang
    Pages 134-141
  12. Juan David Ospina, Oscar Acosta, Gaël Dréan, Guillaume Cazoulat, Antoine Simon, Juan Carlos Correa et al.
    Pages 142-150
  13. Lisa Tang, Ghassan Hamarneh, Tim Bressmann
    Pages 151-158

About these proceedings


This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.


artificial neural network computer assisted surgery graphical model multi-modality support vector machines

Editors and affiliations

  • Kenji Suzuki
    • 1
  • Fei Wang
    • 2
  • Dinggang Shen
    • 3
  • Pingkun Yan
    • 4
  1. 1.The University of ChicagoChicagoUSA
  2. 2.IBM Research AlmadenSan JoseUSA
  3. 3.School of Medicine, Department of Radiology and Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillUSA
  4. 4.Xian Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’anChina

Bibliographic information