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Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Kenji Suzuki

    1. Tokyo Institute of Technology, Yokohama, Japan

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  2. Mauricio Reyes

    1. University of Bern, Bern, Switzerland

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  3. Tanveer Syeda-Mahmood

    1. IBM Research - Almaden, San Jose, USA

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  4. Ender Konukoglu

    1. ETH Zurich, Zürich, Germany

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  5. Ben Glocker

    1. Imperial College London, London, UK

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  6. Roland Wiest

    1. University Hospital of Bern, Bern, Switzerland

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  7. Yaniv Gur

    1. IBM Research - Almaden, San Jose, USA

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  8. Hayit Greenspan

    1. Tel Aviv University, Ramat Aviv, Israel

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  9. 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 11797)

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

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Table of contents (11 papers)

  1. Front Matter

    Pages i-xvi
    Download chapter PDF

  2. Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019)

    1. Front Matter

      Pages 1-1
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    2. Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer’s Disease Classification

      • Fabian Eitel, Kerstin Ritter, for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
      Pages 3-11

    3. UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics

      • Hugo Yeche, Justin Harrison, Tess Berthier
      Pages 12-20

    4. Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis

      • Hyebin Lee, Seong Tae Kim, Yong Man Ro
      Pages 21-29

    5. Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

      • Maxim Pisov, Mikhail Goncharov, Nadezhda Kurochkina, Sergey Morozov, Victor Gombolevskiy, Valeria Chernina et al.
      Pages 30-38

    6. Guideline-Based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules

      • Peifei Zhu, Masahiro Ogino
      Pages 39-47

    7. Deep Neural Network or Dermatologist?

      • Kyle Young, Gareth Booth, Becks Simpson, Reuben Dutton, Sally Shrapnel
      Pages 48-55

    8. Towards Interpretability of Segmentation Networks by Analyzing DeepDreams

      • Vincent Couteaux, Olivier Nempont, Guillaume Pizaine, Isabelle Bloch
      Pages 56-63

  3. 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019)

    1. Front Matter

      Pages 65-65
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    2. Towards Automatic Diagnosis from Multi-modal Medical Data

      • Jiang Tian, Cheng Zhong, Zhongchao Shi, Feiyu Xu
      Pages 67-74

    3. Deep Learning Based Multi-modal Registration for Retinal Imaging

      • Mustafa Arikan, Amir Sadeghipour, Bianca Gerendas, Reinhard Told, Ursula Schmidt-Erfurt
      Pages 75-82

    4. Automated Enriched Medical Concept Generation for Chest X-ray Images

      • Aydan Gasimova
      Pages 83-92

  4. Correction to: Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

    • Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest et al.
    Pages C1-C1

  5. Back Matter

    Pages 93-93
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  1. Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

Keywords

About this book

This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019.

The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. 

Editors and Affiliations

  • Tokyo Institute of Technology, Yokohama, Japan

    Kenji Suzuki

  • University of Bern, Bern, Switzerland

    Mauricio Reyes

  • IBM Research - Almaden, San Jose, USA

    Tanveer Syeda-Mahmood, Yaniv Gur

  • ETH Zurich, Zürich, Germany

    Ender Konukoglu

  • Imperial College London, London, UK

    Ben Glocker

  • University Hospital of Bern, Bern, Switzerland

    Roland Wiest

  • Tel Aviv University, Ramat Aviv, Israel

    Hayit Greenspan

  • Case Western Reserve University, Cleveland, USA

    Anant Madabhushi

Bibliographic Information

  • Book Title: Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

  • Book Subtitle: Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Editors: Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, … Anant Madabhushi

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-33850-3

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-33849-7Published: 26 October 2019

  • eBook ISBN: 978-3-030-33850-3Published: 24 October 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVI, 93

  • Number of Illustrations: 5 b/w illustrations, 35 illustrations in colour

  • Topics: Artificial Intelligence, Mathematical Logic and Formal Languages, Health Informatics, Image Processing and Computer Vision

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