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Understanding and Interpreting Machine Learning in Medical Image Computing Applications

First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings

  • Conference proceedings
  • © 2018

Overview

Editors:
  1. Danail Stoyanov

    1. University College London, London, UK

  2. Zeike Taylor

    1. University of Leeds, Leeds, UK

  3. Seyed Mostafa Kia

    1. Radboud University Medical Center, Nijmegen, The Netherlands

  4. Ipek Oguz

    1. Vanderbilt University, Nashville, USA

  5. Mauricio Reyes

    1. University of Bern, Bern, Switzerland

  6. Anne Martel

    1. Sunnybrook Research Institute, Toronto, Canada

  7. Lena Maier-Hein

    1. German Cancer Research Center (DKFZ), Heidelberg, Germany

  8. Andre F. Marquand

    1. Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

  9. Edouard Duchesnay

    1. NeuroSpin, CEA Saclay, Gif-sur-Yvette, France

  10. Tommy Löfstedt

    1. Umeå University, Umeå, Sweden

  11. Bennett Landman

    1. Vanderbilt University, Nashville, USA

  12. M. Jorge Cardoso

    1. King's College London, London, UK

  13. Carlos A. Silva

    1. University of Minho, Guimarães, Portugal

  14. Sergio Pereira

    1. University of Minho, Guimarães, Portugal

  15. Raphael Meier

    1. University Hospital Inselspital, Bern, Switzerland

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11038)

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

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About this book

This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 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 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identifythe main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.  


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Table of contents (16 papers)

  1. Front Matter

    Pages I-XVI
    Download chapter PDF

  2. First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Alzheimer’s Disease Modelling and Staging Through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes

      • Clement Abi Nader, Nicholas Ayache, Philippe Robert, Marco Lorenzi, for the Alzheimer’s Disease Neuroimaging Initiative
      Pages 3-14

    3. Multi-channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer’s Disease

      • Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi, for the Alzheimer’s Disease Neuroimaging Initiative
      Pages 15-23

    4. Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer’s Disease

      • Johannes Rieke, Fabian Eitel, Martin Weygandt, John-Dylan Haynes, Kerstin Ritter
      Pages 24-31

    5. Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data

      • Roberto Vega, Russ Greiner
      Pages 32-39

  3. First International Workshop on Deep Learning Fails Workshop, DLF 2018

    1. Front Matter

      Pages 41-41
      Download chapter PDF

    2. Towards Robust CT-Ultrasound Registration Using Deep Learning Methods

      • Yuanyuan Sun, Adriaan Moelker, Wiro J. Niessen, Theo van Walsum
      Pages 43-51

    3. To Learn or Not to Learn Features for Deformable Registration?

      • Aabhas Majumdar, Raghav Mehta, Jayanthi Sivaswamy
      Pages 52-60

    4. Evaluation of Strategies for PET Motion Correction - Manifold Learning vs. Deep Learning

      • James R. Clough, Daniel R. Balfour, Claudia Prieto, Andrew J. Reader, Paul K. Marsden, Andrew P. King
      Pages 61-69

    5. Exploring Adversarial Examples

      • David Kügler, Alexander Distergoft, Arjan Kuijper, Anirban Mukhopadhyay
      Pages 70-78

    6. Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks

      • Muhan Shao, Shuo Han, Aaron Carass, Xiang Li, Ari M. Blitz, Jerry L. Prince et al.
      Pages 79-86

    7. Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks

      • Saeid Asgari Taghanaki, Arkadeep Das, Ghassan Hamarneh
      Pages 87-94

  4. First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018

    1. Front Matter

      Pages 95-95
      Download chapter PDF

    2. Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images

      • Noel C. F. Codella, Chung-Ching Lin, Allan Halpern, Michael Hind, Rogerio Feris, John R. Smith
      Pages 97-105

    3. Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment

      • Sérgio Pereira, Raphael Meier, Victor Alves, Mauricio Reyes, Carlos A. Silva
      Pages 106-114

    4. Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification

      • Pieter Van Molle, Miguel De Strooper, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt
      Pages 115-123

    5. Regression Concept Vectors for Bidirectional Explanations in Histopathology

      • Mara Graziani, Vincent Andrearczyk, Henning Müller
      Pages 124-132

    6. Towards Complementary Explanations Using Deep Neural Networks

      • Wilson Silva, Kelwin Fernandes, Maria J. Cardoso, Jaime S. Cardoso
      Pages 133-140

    7. How Users Perceive Content-Based Image Retrieval for Identifying Skin Images

      • Mahya Sadeghi, Parmit K. Chilana, M. Stella Atkins
      Pages 141-148
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Other volumes

  1. Understanding and Interpreting Machine Learning in Medical Image Computing Applications

Editors and Affiliations

  • University College London, London, UK

    Danail Stoyanov

  • University of Leeds, Leeds, UK

    Zeike Taylor

  • Radboud University Medical Center, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  • Vanderbilt University, Nashville, USA

    Ipek Oguz, Bennett Landman

  • University of Bern, Bern, Switzerland

    Mauricio Reyes

  • Sunnybrook Research Institute, Toronto, Canada

    Anne Martel

  • German Cancer Research Center (DKFZ), Heidelberg, Germany

    Lena Maier-Hein

  • Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

    Andre F. Marquand

  • NeuroSpin, CEA Saclay, Gif-sur-Yvette, France

    Edouard Duchesnay

  • UmeÃ¥ University, UmeÃ¥, Sweden

    Tommy Löfstedt

  • King's College London, London, UK

    M. Jorge Cardoso

  • University of Minho, Guimarães, Portugal

    Carlos A. Silva, Sergio Pereira

  • University Hospital Inselspital, Bern, Switzerland

    Raphael Meier

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