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  • Conference proceedings
  • © 2016

Machine Learning in Medical Imaging

7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10019)

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

Conference series link(s): MLMI: International Workshop on Machine Learning in Medical Imaging

Conference proceedings info: MLMI 2016.

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  • ISBN: 978-3-319-47157-0
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Table of contents (38 papers)

  1. Front Matter

    Pages I-XIV
  2. Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph

    • Chen Zu, Yue Gao, Brent Munsell, Minjeong Kim, Ziwen Peng, Yingying Zhu et al.
    Pages 1-9
  3. Bilateral Regularization in Reproducing Kernel Hilbert Spaces for Discontinuity Preserving Image Registration

    • Christoph Jud, Nadia Möri, Benedikt Bitterli, Philippe C. Cattin
    Pages 10-17
  4. Building an Ensemble of Complementary Segmentation Methods by Exploiting Probabilistic Estimates

    • Gerard Sanroma, Oualid M. Benkarim, Gemma Piella, Miguel Ángel González Ballester
    Pages 27-35
  5. Learning Appearance and Shape Evolution for Infant Image Registration in the First Year of Life

    • Lifang Wei, Shunbo Hu, Yaozong Gao, Xiaohuan Cao, Guorong Wu, Dinggang Shen
    Pages 36-44
  6. Detecting Osteophytes in Radiographs of the Knee to Diagnose Osteoarthritis

    • Jessie Thomson, Terence O’Neill, David Felson, Tim Cootes
    Pages 45-52
  7. Segmentation of Perivascular Spaces Using Vascular Features and Structured Random Forest from 7T MR Image

    • Jun Zhang, Yaozong Gao, Sang Hyun Park, Xiaopeng Zong, Weili Lin, Dinggang Shen
    Pages 61-68
  8. Dual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images

    • Minjeong Kim, Guorong Wu, Isrem Rekik, Dinggang Shen
    Pages 69-76
  9. Joint Discriminative and Representative Feature Selection for Alzheimer’s Disease Diagnosis

    • Xiaofeng Zhu, Heung-Il Suk, Kim-Han Thung, Yingying Zhu, Guorong Wu, Dinggang Shen
    Pages 77-85
  10. Patch-Based Hippocampus Segmentation Using a Local Subspace Learning Method

    • Yan Wang, Xi Wu, Guangkai Ma, Zongqing Ma, Ying Fu, Jiliu Zhou
    Pages 86-94
  11. A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation

    • Chen Qin, Ricardo Guerrero Moreno, Christopher Bowles, Christian Ledig, Philip Scheltens, Frederik Barkhof et al.
    Pages 104-112
  12. Learning Representation for Histopathological Image with Quaternion Grassmann Average Network

    • Jinjie Wu, Jun Shi, Shihui Ying, Qi Zhang, Yan Li
    Pages 122-129
  13. Learning Global and Cluster-Specific Classifiers for Robust Brain Extraction in MR Data

    • Yuan Liu, Hasan E. Çetingül, Benjamin L. Odry, Mariappan S. Nadar
    Pages 130-138
  14. Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Pattern Detection

    • Mingchen Gao, Ziyue Xu, Le Lu, Adam P. Harrison, Ronald M. Summers, Daniel J. Mollura
    Pages 147-155
  15. Segmentation-Free Estimation of Kidney Volumes in CT with Dual Regression Forests

    • Mohammad Arafat Hussain, Ghassan Hamarneh, Timothy W. O’Connell, Mohammed F. Mohammed, Rafeef Abugharbieh
    Pages 156-163

Other Volumes

  1. Machine Learning in Medical Imaging

    7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings

About this book

This book constitutes the refereed proceedings of the 7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016, held in conjunction with MICCAI 2016, in Athens, Greece, in October 2016.

The 38 full papers presented in this volume were carefully reviewed and selected from 60 submissions.

The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

Keywords

  • computer-aided diagnosis
  • image processing
  • machine learning
  • neural networks
  • supervised learning
  • 3D ultrasound
  • brain MRI
  • classification
  • deep learning
  • dimensionality reduction
  • ensemble learning
  • factorization methods
  • image segmentation
  • kernel methods
  • manifold learning
  • medical image analysis
  • multi-task learning
  • regression trees
  • sensorless reconstruction
  • shape representation

Editors and Affiliations

  • University of North Carolina , Chapel Hill, USA

    Li Wang, Ehsan Adeli

  • Shanghai Jiaotong University , Shanghai, China

    Qian Wang

  • Nanjing University , Nanjing, China

    Yinghuan Shi

  • Korea University , Seoul, Korea (Republic of)

    Heung-Il Suk

Bibliographic Information

Buying options

eBook USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-47157-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 79.99
Price excludes VAT (USA)