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Machine Learning in Medical Imaging

8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. Qian Wang

    1. Shanghai Jiao Tong University, Shanghai, China

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  2. Yinghuan Shi

    1. Nanjing University , Nanjing, China

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  3. Heung-Il Suk

    1. Korea University , Seoul, Korea (Republic of)

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  4. Kenji Suzuki

    1. Illinois Institute of Technology, Chicago, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10541)

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 2017.

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Table of contents (44 papers)

  1. Front Matter

    Pages VII-XIX
    Download chapter PDF

  2. From Large to Small Organ Segmentation in CT Using Regional Context

    • Marie Bieth, Esther Alberts, Markus Schwaiger, Bjoern Menze
    Pages 1-9

  3. Motion Corruption Detection in Breast DCE-MRI

    • Sylvester Chiang, Sharmila Balasingham, Lara Richmond, Belinda Curpen, Mia Skarpathiotakis, Anne Martel
    Pages 10-18

  4. Detection and Localization of Drosophila Egg Chambers in Microscopy Images

    • Jiří Borovec, Jan Kybic, Rodrigo Nava
    Pages 19-26

  5. Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring

    • Felix Durlak, Michael Wels, Chris Schwemmer, Michael Sühling, Stefan Steidl, Andreas Maier
    Pages 27-35

  6. Atlas of Classifiers for Brain MRI Segmentation

    • Boris Kodner, Shiri Gordon, Jacob Goldberger, Tammy Riklin Raviv
    Pages 36-44

  7. Dictionary Learning and Sparse Coding-Based Denoising for High-Resolution Task Functional Connectivity MRI Analysis

    • Seongah Jeong, Xiang Li, Jiarui Yang, Quanzheng Li, Vahid Tarokh
    Pages 45-52

  8. Yet Another ADNI Machine Learning Paper? Paving the Way Towards Fully-Reproducible Research on Classification of Alzheimer’s Disease

    • Jorge Samper-González, Ninon Burgos, Sabrina Fontanella, Hugo Bertin, Marie-Odile Habert, Stanley Durrleman et al.
    Pages 53-60

  9. Multi-factorial Age Estimation from Skeletal and Dental MRI Volumes

    • Darko Štern, Philipp Kainz, Christian Payer, Martin Urschler
    Pages 61-69

  10. Automatic Classification of Proximal Femur Fractures Based on Attention Models

    • Anees Kazi, Shadi Albarqouni, Amelia Jimenez Sanchez, Sonja Kirchhoff, Peter Biberthaler, Nassir Navab et al.
    Pages 70-78

  11. Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation

    • Fahdi Kanavati, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert, Ben Glocker
    Pages 79-87

  12. Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble

    • Yani Chen, Bibo Shi, Zhewei Wang, Tao Sun, Charles D. Smith, Jundong Liu
    Pages 88-96

  13. STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion

    • Yao Xiao, Ajay Gupta, Pina C. Sanelli, Ruogu Fang
    Pages 97-105

  14. Classification of Alzheimer’s Disease by Cascaded Convolutional Neural Networks Using PET Images

    • Danni Cheng, Manhua Liu
    Pages 106-113

  15. Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images

    • Yuru Pei, Yunai Yi, Gengyu Ma, Yuke Guo, Gui Chen, Tianmin Xu et al.
    Pages 114-122

  16. Multi-scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base

    • Yuru Pei, Haifang Qin, Gengyu Ma, Yuke Guo, Gui Chen, Tianmin Xu et al.
    Pages 123-131

  17. Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementia Diagnosis

    • Tao Zhou, Kim-Han Thung, Xiaofeng Zhu, Dinggang Shen
    Pages 132-140

  18. 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels

    • Dakai Jin, Ziyue Xu, Adam P. Harrison, Kevin George, Daniel J. Mollura
    Pages 141-149

  19. Efficient Groupwise Registration for Brain MRI by Fast Initialization

    • Pei Dong, Xiaohuan Cao, Jun Zhang, Minjeong Kim, Guorong Wu, Dinggang Shen
    Pages 150-158

  20. Sparse Multi-view Task-Centralized Learning for ASD Diagnosis

    • Jun Wang, Qian Wang, Shitong Wang, Dinggang Shen
    Pages 159-167
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  1. Machine Learning in Medical Imaging

Keywords

About this book

This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017.

The 44 full papers presented in this volume were carefully reviewed and selected from 63 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.

Editors and Affiliations

  • Shanghai Jiao Tong University, Shanghai, China

    Qian Wang

  • Nanjing University , Nanjing, China

    Yinghuan Shi

  • Korea University , Seoul, Korea (Republic of)

    Heung-Il Suk

  • Illinois Institute of Technology, Chicago, USA

    Kenji Suzuki

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