Advertisement

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

  • Kenji Suzuki
  • Mauricio Reyes
  • Tanveer Syeda-Mahmood
  • ETH Zurich
  • Ben Glocker
  • Roland Wiest
  • Yaniv Gur
  • Hayit Greenspan
  • Anant Madabhushi
Conference proceedings ML-CDS 2019, IMIMIC 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11797)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11797)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019)

    1. Front Matter
      Pages 1-1
    2. Fabian Eitel, Kerstin Ritter, for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
      Pages 3-11
    3. Maxim Pisov, Mikhail Goncharov, Nadezhda Kurochkina, Sergey Morozov, Victor Gombolevsky, Valeria Chernina et al.
      Pages 30-38
    4. Kyle Young, Gareth Booth, Becks Simpson, Reuben Dutton, Sally Shrapnel
      Pages 48-55
    5. 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
    2. Jiang Tian, Cheng Zhong, Zhongchao Shi, Feiyu Xu
      Pages 67-74
    3. Mustafa Arikan, Amir Sadeghipour, Bianca Gerendas, Reinhard Told, Ursula Schmidt-Erfurt
      Pages 75-82
  4. Back Matter
    Pages 93-93

About these proceedings

Introduction

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. 

Keywords

artificial intelligence clinical decision support computer assisted intervention deep learning fuzzy control fuzzy logic fuzzy models fuzzy rules fuzzy sets fuzzy systems image analysis image processing interpretability linguistics machine learning medical imaging multi-modal learning neural networks semantics

Editors and affiliations

  • Kenji Suzuki
    • 1
  • Mauricio Reyes
    • 2
  • Tanveer Syeda-Mahmood
    • 3
  • ETH Zurich
    • 4
  • Ben Glocker
    • 5
  • Roland Wiest
    • 6
  • Yaniv Gur
    • 7
  • Hayit Greenspan
    • 8
  • Anant Madabhushi
    • 9
  1. 1.Tokyo Institute of TechnologyYokohamaJapan
  2. 2.University of BernBernSwitzerland
  3. 3.IBM Research - AlmadenSan JoseUSA
  4. 4.ETH ZurichZurichGermany
  5. 5.Imperial College LondonLondonUK
  6. 6.University Hospital of BernBernSwitzerland
  7. 7.IBM Research - AlmadenSan JoseUSA
  8. 8.Tel Aviv UniversityRamat AvivIsrael
  9. 9.Case Western Reserve UniversityClevelandUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-33850-3
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-33849-7
  • Online ISBN 978-3-030-33850-3
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
Industry Sectors
Pharma
Materials & Steel
Automotive
Chemical Manufacturing
Health & Hospitals
Biotechnology
Finance, Business & Banking
Electronics
IT & Software
Telecommunications
Consumer Packaged Goods
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering