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Deep Learning and Data Labeling for Medical Applications

First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings

  • Conference proceedings
  • © 2016

Overview

Editors:
  1. Gustavo Carneiro

    1. University of Adelaide, Adelaide, Australia

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  2. Diana Mateus

    1. Technical University of Munich, Garching, Germany

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  3. Loïc Peter

    1. Technical University of Munich, Garching, Germany

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  4. Andrew Bradley

    1. University of Queensland, St Lucia, Australia

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  5. João Manuel R. S. Tavares

    1. Universidade do Porto, Porto, Portugal

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  6. Vasileios Belagiannis

    1. University of Oxford, Oxford, United Kingdom

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  7. João Paulo Papa

    1. Universidade Estadual Paulista, Bauru, Brazil

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  8. Jacinto C. Nascimento

    1. Instituto Superior Técnico, Lisbon, Portugal

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  9. Marco Loog

    1. Delft University of Technology, Delft, The Netherlands

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  10. Zhi Lu

    1. University of South Australia, Adelaide, Australia

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  11. Jaime S. Cardoso

    1. Universidade do Porto, Porto, Portugal

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  12. Julien Cornebise

    1. Google DeepMind, London, United Kingdom

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10008)

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

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Table of contents (29 papers)

  1. Front Matter

    Pages I-XIII
    Download chapter PDF

  2. Deep Learning in Medical Image Analysis

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNs

      • Xian-Hua Han, Jianmei Lei, Yen-Wei Chen
      Pages 3-11

    3. Robust 3D Organ Localization with Dual Learning Architectures and Fusion

      • Xiaoguang Lu, Daguang Xu, David Liu
      Pages 12-20

    4. Cell Segmentation Proposal Network for Microscopy Image Analysis

      • Saad Ullah Akram, Juho Kannala, Lauri Eklund, Janne Heikkilä
      Pages 21-29

    5. Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks

      • Erik Smistad, Lasse Løvstakken
      Pages 30-38

    6. Convolutional Neural Network for Reconstruction of 7T-like Images from 3T MRI Using Appearance and Anatomical Features

      • Khosro Bahrami, Feng Shi, Islem Rekik, Dinggang Shen
      Pages 39-47

    7. Fast Predictive Image Registration

      • Xiao Yang, Roland Kwitt, Marc Niethammer
      Pages 48-57

    8. Longitudinal Multiple Sclerosis Lesion Segmentation Using Multi-view Convolutional Neural Networks

      • Ariel Birenbaum, Hayit Greenspan
      Pages 58-67

    9. Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks

      • Daniel E. Worrall, Clare M. Wilson, Gabriel J. Brostow
      Pages 68-76

    10. Fully Convolutional Network for Liver Segmentation and Lesions Detection

      • Avi Ben-Cohen, Idit Diamant, Eyal Klang, Michal Amitai, Hayit Greenspan
      Pages 77-85

    11. Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis

      • Youngjin Yoo, Lisa W. Tang, Tom Brosch, David K. B. Li, Luanne Metz, Anthony Traboulsee et al.
      Pages 86-94

    12. De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks

      • Ariel Benou, Ronel Veksler, Alon Friedman, Tammy Riklin Raviv
      Pages 95-110

    13. Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majority Voting

      • Xiangrong Zhou, Takaaki Ito, Ryosuke Takayama, Song Wang, Takeshi Hara, Hiroshi Fujita
      Pages 111-120

    14. Medical Image Description Using Multi-task-loss CNN

      • Pavel Kisilev, Eli Sason, Ella Barkan, Sharbell Hashoul
      Pages 121-129

    15. Fully Automating Graf’s Method for DDH Diagnosis Using Deep Convolutional Neural Networks

      • David Golan, Yoni Donner, Chris Mansi, Jacob Jaremko, Manoj Ramachandran, on behalf of CUDL
      Pages 130-141

    16. Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data

      • Simon Andermatt, Simon Pezold, Philippe Cattin
      Pages 142-151

    17. Learning Thermal Process Representations for Intraoperative Analysis of Cortical Perfusion During Ischemic Strokes

      • Nico Hoffmann, Edmund Koch, Gerald Steiner, Uwe Petersohn, Matthias Kirsch
      Pages 152-160

    18. Automatic Slice Identification in 3D Medical Images with a ConvNet Regressor

      • Bob D. de Vos, Max A. Viergever, Pim A. de Jong, Ivana Išgum
      Pages 161-169

    19. Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks

      • Dong Nie, Xiaohuan Cao, Yaozong Gao, Li Wang, Dinggang Shen
      Pages 170-178
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  1. Deep Learning and Data Labeling for Medical Applications

Keywords

About this book

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

Editors and Affiliations

  • University of Adelaide, Adelaide, Australia

    Gustavo Carneiro

  • Technical University of Munich, Garching, Germany

    Diana Mateus, Loïc Peter

  • University of Queensland, St Lucia, Australia

    Andrew Bradley

  • Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares, Jaime S. Cardoso

  • University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  • Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  • Instituto Superior Técnico, Lisbon, Portugal

    Jacinto C. Nascimento

  • Delft University of Technology, Delft, The Netherlands

    Marco Loog

  • University of South Australia, Adelaide, Australia

    Zhi Lu

  • Google DeepMind, London, United Kingdom

    Julien Cornebise

Bibliographic Information

  • Book Title: Deep Learning and Data Labeling for Medical Applications

  • Book Subtitle: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings

  • Editors: Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, … Julien Cornebise

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-319-46976-8

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer International Publishing AG 2016

  • Softcover ISBN: 978-3-319-46975-1Published: 27 September 2016

  • eBook ISBN: 978-3-319-46976-8Published: 07 October 2016

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIII, 280

  • Number of Illustrations: 115 b/w illustrations

  • Topics: Image Processing and Computer Vision, Pattern Recognition, Artificial Intelligence, Computer Graphics, Health Informatics

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