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Machine Learning in Medical Imaging

6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings

  • Conference proceedings
  • © 2015

Overview

Editors:
  1. Luping Zhou

    1. School of Computing and Information Technology, University of Wollongong, Wollongong, Australia

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  2. Li Wang

    1. Radiology and BRIC, University of North Carolina, Chapel Hill, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Qian Wang

    1. Biomedical Engineering, Shanghai Jiaotong University, Shanghai, China

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  4. Yinghuan Shi

    1. Computer Science, Nanjing University, Nanjing, China

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 9352)

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 2015.

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Table of contents (40 papers)

  1. Front Matter

    Pages I-XII
    Download chapter PDF

  2. Segmentation of Right Ventricle in Cardiac MR Images Using Shape Regression

    • Suman Sedai, Pallab Roy, Rahil Garnavi
    Pages 1-8

  3. Visual Saliency Based Active Learning for Prostate MRI Segmentation

    • Dwarikanath Mahapatra, Joachim M. Buhmann
    Pages 9-16

  4. Soft-Split Random Forest for Anatomy Labeling

    • Guangkai Ma, Yaozong Gao, Li Wang, Ligang Wu, Dinggang Shen
    Pages 17-25

  5. A New Image Data Set and Benchmark for Cervical Dysplasia Classification Evaluation

    • Tao Xu, Cheng Xin, L. Rodney Long, Sameer Antani, Zhiyun Xue, Edward Kim et al.
    Pages 26-35

  6. Machine Learning on High Dimensional Shape Data from Subcortical Brain Surfaces: A Comparison of Feature Selection and Classification Methods

    • Benjamin S. C. Wade, Shantanu H. Joshi, Boris A. Gutman, Paul M. Thompson
    Pages 36-43

  7. Node-Based Gaussian Graphical Model for Identifying Discriminative Brain Regions from Connectivity Graphs

    • Bernard Ng, Anna-Clare Milazzo, Andre Altmann
    Pages 44-51

  8. BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease

    • Mohammad Khatami, Tobias Schmidt-Wilcke, Pia C. Sundgren, Amin Abbasloo, Bernhard Schölkopf, Thomas Schultz
    Pages 52-60

  9. FADR: Functional-Anatomical Discriminative Regions for Rest fMRI Characterization

    • Marta Nuñez-Garcia, Sonja Simpraga, Maria Angeles Jurado, Maite Garolera, Roser Pueyo, Laura Igual
    Pages 61-68

  10. Craniomaxillofacial Deformity Correction via Sparse Representation in Coherent Space

    • Zuoyong Li, Le An, Jun Zhang, Li Wang, James J. Xia, Dinggang Shen
    Pages 69-76

  11. Nonlinear Graph Fusion for Multi-modal Classification of Alzheimer’s Disease

    • Tong Tong, Katherine Gray, Qinquan Gao, Liang Chen, Daniel Rueckert
    Pages 77-84

  12. HEp-2 Staining Pattern Recognition Using Stacked Fisher Network for Encoding Weber Local Descriptor

    • Xian-Hua Han, Yen-Wei Chen, Gang Xu
    Pages 85-93

  13. Supervoxel Classification Forests for Estimating Pairwise Image Correspondences

    • Fahdi Kanavati, Tong Tong, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert et al.
    Pages 94-101

  14. Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model

    • Guoyan Zheng, Weimin Yu
    Pages 102-109

  15. Learning and Combining Image Similarities for Neonatal Brain Population Studies

    • Veronika A. Zimmer, Ben Glocker, Paul Aljabar, Serena J. Counsell, Mary A. Rutherford, A. David Edwards et al.
    Pages 110-117

  16. Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images

    • Noel Codella, Junjie Cai, Mani Abedini, Rahil Garnavi, Alan Halpern, John R. Smith
    Pages 118-126

  17. Predicting Standard-Dose PET Image from Low-Dose PET and Multimodal MR Images Using Mapping-Based Sparse Representation

    • Yan Wang, Pei Zhang, Le An, Guangkai Ma, Jiayin Kang, Xi Wu et al.
    Pages 127-135

  18. Boosting Convolutional Filters with Entropy Sampling for Optic Cup and Disc Image Segmentation from Fundus Images

    • Julian G. Zilly, Joachim M. Buhmann, Dwarikanath Mahapatra
    Pages 136-143

  19. Brain Fiber Clustering Using Non-negative Kernelized Matching Pursuit

    • Kuldeep Kumar, Christian Desrosiers, Kaleem Siddiqi
    Pages 144-152

  20. Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features

    • Atsuki Masuda, Bisser Raytchev, Takio Kurita, Toru Imamura, Masashi Suzuki, Toru Tamaki et al.
    Pages 153-160
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  1. Machine Learning in Medical Imaging

Keywords

About this book

This book constitutes the proceedings of the 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015, held in conjunction with MICCAI 2015, in Munich in October 2015.

The 40 full papers presented in this volume were carefully reviewed and selected from 69 submissions. The workshop focuses on major trends and challenges in the area of machine learning in medical imaging and present works aimed to identify new cutting-edge techniques and their use in medical imaging. 

Editors and Affiliations

  • School of Computing and Information Technology, University of Wollongong, Wollongong, Australia

    Luping Zhou

  • Radiology and BRIC, University of North Carolina, Chapel Hill, USA

    Li Wang

  • Biomedical Engineering, Shanghai Jiaotong University, Shanghai, China

    Qian Wang

  • Computer Science, Nanjing University, Nanjing, China

    Yinghuan Shi

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