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Machine Learning in Medical Imaging

5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 14, 2014, Proceedings

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Guorong Wu

    1. University of North Carolina at Chapel Hill, Chapel Hill, USA

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  2. Daoqiang Zhang

    1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

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  3. Luping Zhou

    1. University of Wollongong, Wollongong, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8679)

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

Included in the following conference series:

Conference proceedings info: MLMI 2014.

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Table of contents (40 papers)

  1. Front Matter

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  2. Sparsity-Learning-Based Longitudinal MR Image Registration for Early Brain Development

    • Qian Wang, Guorong Wu, Li Wang, Pengfei Shi, Weili Lin, Dinggang Shen
    Pages 1-8

  3. Graph-Based Label Propagation in Fetal Brain MR Images

    • Lisa M. Koch, Robert Wright, Deniz Vatansever, Vanessa Kyriakopoulou, Christina Malamateniou, Prachi A. Patkee et al.
    Pages 9-16

  4. Deep Learning Based Automatic Immune Cell Detection for Immunohistochemistry Images

    • Ting Chen, Christophe Chefd’hotel
    Pages 17-24

  5. Stacked Multiscale Feature Learning for Domain Independent Medical Image Segmentation

    • Ryan Kiros, Karteek Popuri, Dana Cobzas, Martin Jagersand
    Pages 25-32

  6. Detection of Mammographic Masses by Content-Based Image Retrieval

    • Menglin Jiang, Shaoting Zhang, Dimitris N. Metaxas
    Pages 33-41

  7. Inferring Sources of Dementia Progression with Network Diffusion Model

    • Chenhui Hu, Xue Hua, Paul M. Thompson, Georges El Fakhri, Quanzheng Li
    Pages 42-49

  8. 3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

    • Cheng Chen, D. Belavy, Guoyan Zheng
    Pages 50-58

  9. Exploring Compact Representation of SICE Matrices for Functional Brain Network Classification

    • Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li
    Pages 59-67

  10. Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression

    • Zhen Yang, Shenghua Zhong, Aaron Carass, Sarah H. Ying, Jerry L. Prince
    Pages 68-76

  11. Manifold Alignment and Transfer Learning for Classification of Alzheimer’s Disease

    • Ricardo Guerrero, Christian Ledig, Daniel Rueckert
    Pages 77-84

  12. Gleason Grading of Prostate Tumours with Max-Margin Conditional Random Fields

    • Joseph G. Jacobs, Eleftheria Panagiotaki, Daniel C. Alexander
    Pages 85-92

  13. Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images

    • Yaozong Gao, Li Wang, Yeqin Shao, Dinggang Shen
    Pages 93-100

  14. Geodesic Geometric Mean of Regional Covariance Descriptors as an Image-Level Descriptor for Nuclear Atypia Grading in Breast Histology Images

    • Adnan Mujahid Khan, Korsuk Sirinukunwattana, Nasir Rajpoot
    Pages 101-108

  15. A Constrained Regression Forests Solution to 3D Fetal Ultrasound Plane Localization for Longitudinal Analysis of Brain Growth and Maturation

    • Mohammad Yaqub, Anil Kopuri, Sylvia Rueda, Peter B. Sullivan, Kenneth McCormick, J. Alison Noble
    Pages 109-116

  16. Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation

    • Youngjin Yoo, Tom Brosch, Anthony Traboulsee, David K. B. Li, Roger Tam
    Pages 117-124

  17. Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer

    • Hao Chen, Dong Ni, Xin Yang, Shengli Li, Pheng Ann Heng
    Pages 125-132

  18. Searching for Structures of Interest in an Ultrasound Video Sequence

    • Mohammad Ali Maraci, Raffaele Napolitano, Aris Papageorghiou, J. Alison Noble
    Pages 133-140

  19. Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer’s Disease

    • Mawulawoé Komlagan, Vinh-Thong Ta, Xingyu Pan, Jean-Philippe Domenger, D. Louis Collins, Pierrick Coupé et al.
    Pages 141-148

  20. Automatic Bone and Marrow Extraction from Dual Energy CT through SVM Margin-Based Multi-Material Decomposition Model Selection

    • Harini Veeraraghavan, Duc Fehr, Ross Schmidtlein, Sinchun Hwang, Joseph O. Deasy
    Pages 149-156
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  1. Machine Learning in Medical Imaging

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About this book

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Medical Imaging, MLMI 2014, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, in Cambridge, MA, USA, in September 2014. The 40 contributions included in this volume were carefully reviewed and selected from 70 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.

Editors and Affiliations

  • University of North Carolina at Chapel Hill, Chapel Hill, USA

    Guorong Wu

  • Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  • University of Wollongong, Wollongong, Australia

    Luping Zhou

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