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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

  • Conference proceedings
  • © 2018

Overview

Editors:
  1. Danail Stoyanov

    1. University College London, London, UK

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  2. Zeike Taylor

    1. University of Leeds, Leeds, UK

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  3. Gustavo Carneiro

    1. University of Adelaide, Adelaide, Australia

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  4. Tanveer Syeda-Mahmood

    1. IBM Research – Almaden, San Jose, USA

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  5. Anne Martel

    1. Sunnybrook Health Science Centre, Toronto, Canada

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  6. Lena Maier-Hein

    1. Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

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  7. João Manuel R.S. Tavares

    1. University of Porto, Porto, Portugal

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  8. Andrew Bradley

    1. Queensland University of Technology, Brisbane, Australia

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  9. João Paulo Papa

    1. Universidade Estadual Paulista, Bauru, Brazil

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  10. Vasileios Belagiannis

    1. OSRAM (Germany), Garching b. München, Germany

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  11. Jacinto C. Nascimento

    1. University of Lisbon, Lisboa, Portugal

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  12. Zhi Lu

    1. ReFUEL4, Singapore, Singapore

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  13. Sailesh Conjeti

    1. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany

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  14. Mehdi Moradi

    1. IBM Research – Almaden, San Jose, USA

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  15. Hayit Greenspan

    1. Tel Aviv University, Tel Aviv, Israel

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  16. Anant Madabhushi

    1. Case Western Reserve University, Cleveland, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11045)

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

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Table of contents (44 papers)

  1. Front Matter

    Pages I-XVII
    Download chapter PDF

  2. 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. UNet++: A Nested U-Net Architecture for Medical Image Segmentation

      • Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang
      Pages 3-11

    3. Deep Semi-supervised Segmentation with Weight-Averaged Consistency Targets

      • Christian S. Perone, Julien Cohen-Adad
      Pages 12-19

    4. Handling Missing Annotations for Semantic Segmentation with Deep ConvNets

      • Olivier Petit, Nicolas Thome, Arnaud Charnoz, Alexandre Hostettler, Luc Soler
      Pages 20-28

    5. A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data

      • Mohammad H. Jafari, Hany Girgis, Zhibin Liao, Delaram Behnami, Amir Abdi, Hooman Vaseli et al.
      Pages 29-37

    6. Multi-scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification

      • Liying Peng, Lanfen Lin, Hongjie Hu, Huali Li, Qingqing Chen, Dan Wang et al.
      Pages 38-46

    7. TreeNet: Multi-loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees

      • Mengliu Zhao, Ghassan Hamarneh
      Pages 47-55

    8. 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation

      • Zhou He, Siqi Bao, Albert Chung
      Pages 56-64

    9. Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography

      • Delaram Behnami, Christina Luong, Hooman Vaseli, Amir Abdi, Hany Girgis, Dale Hawley et al.
      Pages 65-73

    10. MTMR-Net: Multi-task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis

      • Lihao Liu, Qi Dou, Hao Chen, Iyiola E. Olatunji, Jing Qin, Pheng-Ann Heng
      Pages 74-82

    11. Active Deep Learning with Fisher Information for Patch-Wise Semantic Segmentation

      • Jamshid Sourati, Ali Gholipour, Jennifer G. Dy, Sila Kurugol, Simon K. Warfield
      Pages 83-91

    12. Contextual Additive Networks to Efficiently Boost 3D Image Segmentations

      • Zhenlin Xu, Zhengyang Shen, Marc Niethammer
      Pages 92-100

    13. Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration

      • Julian Krebs, Tommaso Mansi, Boris Mailhé, Nicholas Ayache, Hervé Delingette
      Pages 101-109

    14. Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-organizing Maps

      • Lee B. Reid, Alex M. Pagnozzi
      Pages 110-118

    15. Focal Dice Loss and Image Dilation for Brain Tumor Segmentation

      • Pei Wang, Albert C. S. Chung
      Pages 119-127

    16. Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning

      • Roman Spilger, Thomas Wollmann, Yu Qiang, Andrea Imle, Ji Young Lee, Barbara Müller et al.
      Pages 128-136

    17. 3D Convolutional Neural Networks for Classification of Functional Connectomes

      • Meenakshi Khosla, Keith Jamison, Amy Kuceyeski, Mert R. Sabuncu
      Pages 137-145

    18. Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks

      • Liesbeth Vandewinckele, David Robben, Wouter Crijns, Frederik Maes
      Pages 146-154

    19. Unpaired Deep Cross-Modality Synthesis with Fast Training

      • Lei Xiang, Yang Li, Weili Lin, Qian Wang, Dinggang Shen
      Pages 155-164
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  1. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Keywords

About this book

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.

The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Editors and Affiliations

  • University College London, London, UK

    Danail Stoyanov

  • University of Leeds, Leeds, UK

    Zeike Taylor

  • University of Adelaide, Adelaide, Australia

    Gustavo Carneiro

  • IBM Research – Almaden, San Jose, USA

    Tanveer Syeda-Mahmood, Mehdi Moradi

  • Sunnybrook Health Science Centre, Toronto, Canada

    Anne Martel

  • Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

    Lena Maier-Hein

  • University of Porto, Porto, Portugal

    João Manuel R.S. Tavares

  • Queensland University of Technology, Brisbane, Australia

    Andrew Bradley

  • Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  • OSRAM (Germany), Garching b. München, Germany

    Vasileios Belagiannis

  • University of Lisbon, Lisboa, Portugal

    Jacinto C. Nascimento

  • ReFUEL4, Singapore, Singapore

    Zhi Lu

  • German Center for Neurodegenerative Diseases (DZNE), Munich, Germany

    Sailesh Conjeti

  • Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  • Case Western Reserve University, Cleveland, USA

    Anant Madabhushi

Bibliographic Information

  • Book Title: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

  • Book Subtitle: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

  • Editors: Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood, Anne Martel, Lena Maier-Hein, João Manuel R.S. Tavares, Andrew Bradley, João Paulo Papa, Vasileios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, … Anant Madabhushi

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-00889-5

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2018

  • Softcover ISBN: 978-3-030-00888-8Published: 20 September 2018

  • eBook ISBN: 978-3-030-00889-5Published: 19 September 2018

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVII, 387

  • Number of Illustrations: 48 b/w illustrations, 149 illustrations in colour

  • Topics: Artificial Intelligence, Health Informatics, Computers and Education, Computer Appl. in Social and Behavioral Sciences, Systems and Data Security

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