Machine Learning in Medical Imaging

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

  • Li Wang
  • Ehsan Adeli
  • Qian Wang
  • Yinghuan Shi
  • Heung-Il Suk
Conference proceedings MLMI 2016

DOI: 10.1007/978-3-319-47157-0

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

Table of contents

  1. Front Matter
    Pages I-XIV
    PDF
  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. Do We Need Large Annotated Training Data for Detection Applications in Biomedical Imaging? A Case Study in Renal Glomeruli Detection
    Michael Gadermayr, Barbara Mara Klinkhammer, Peter Boor, Dorit Merhof
    Pages 18-26
  5. 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
  6. 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
  7. Detecting Osteophytes in Radiographs of the Knee to Diagnose Osteoarthritis
    Jessie Thomson, Terence O’Neill, David Felson, Tim Cootes
    Pages 45-52
  8. Direct Estimation of Fiber Orientations Using Deep Learning in Diffusion Imaging
    Simon Koppers, Dorit Merhof
    Pages 53-60
  9. 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
  10. Dual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images
    Minjeong Kim, Guorong Wu, Isrem Rekik, Dinggang Shen
    Pages 69-76
  11. 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
  12. 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
  13. Improving Single-Modal Neuroimaging Based Diagnosis of Brain Disorders via Boosted Privileged Information Learning Framework
    Xiao Zheng, Jun Shi, Shihui Ying, Qi Zhang, Yan Li
    Pages 95-103
  14. 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
  15. Deep Ensemble Sparse Regression Network for Alzheimer’s Disease Diagnosis
    Heung-Il Suk, Dinggang Shen
    Pages 113-121
  16. Learning Representation for Histopathological Image with Quaternion Grassmann Average Network
    Jinjie Wu, Jun Shi, Shihui Ying, Qi Zhang, Yan Li
    Pages 122-129
  17. 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
  18. Cross-Modality Anatomical Landmark Detection Using Histograms of Unsigned Gradient Orientations and Atlas Location Autocontext
    Alison O’Neil, Mohammad Dabbah, Ian Poole
    Pages 139-146
  19. 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
  20. 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

About these proceedings

Introduction

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

  • Li Wang
    • 1
  • Ehsan Adeli
    • 2
  • Qian Wang
    • 3
  • Yinghuan Shi
    • 4
  • Heung-Il Suk
    • 5
  1. 1.University of North Carolina Chapel HillUSA
  2. 2.University of North Carolina Chapel HillUSA
  3. 3.Shanghai Jiaotong University ShanghaiChina
  4. 4.Nanjing University NanjingChina
  5. 5.Korea University SeoulKorea (Republic of)

Bibliographic information

  • Copyright Information Springer International Publishing AG 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-47156-3
  • Online ISBN 978-3-319-47157-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349