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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Hayit Greenspan

    1. Tel Aviv University, Tel Aviv, Israel

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  2. Ryutaro Tanno

    1. University College London, London, UK

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  3. Marius Erdt

    1. Fraunhofer Singapore, Nanyang Technological University, Singapore, Singapore

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  4. Tal Arbel

    1. McGill University, Montreal, Canada

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  5. Christian Baumgartner

    1. ETH Zürich, Zürich, Switzerland

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  6. Adrian Dalca

    1. Massachusetts Institute of Technology, Harvard Medical School, Cambridge, USA

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  7. Carole H. Sudre

    1. University College London, King's College London, London, UK

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  8. William M. Wells

    1. Harvard Medical School, Boston, USA

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  9. Klaus Drechsler

    1. Aachen University of Applied Sciences, Aachen, Germany

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  10. Marius George Linguraru

    1. Children’s National Healthcare System, Washington, D.C., USA

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  11. Cristina Oyarzun Laura

    1. Fraunhofer IGD, Darmstadt, Germany

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  12. Raj Shekhar

    1. Children's National Healthcare System, Washington, D.C., USA

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  13. Stefan Wesarg

    1. Fraunhofer IGD, Darmstadt, Germany

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  14. Miguel Ángel González Ballester

    1. ICREA - Universitat Pompeu Fabra, Barcelona, Spain

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11840)

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

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Table of contents (19 papers)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. UNSURE 2019: Uncertainty Quantification and Noise Modelling

    1. Front Matter

      Pages 1-1
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    2. Probabilistic Surface Reconstruction with Unknown Correspondence

      • Dennis Madsen, Thomas Vetter, Marcel Lüthi
      Pages 3-11

    3. Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty

      • Alireza Sedghi, Tina Kapur, Jie Luo, Parvin Mousavi, William M. Wells III
      Pages 12-22

    4. Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference

      • Raghav Mehta, Thomas Christinck, Tanya Nair, Paul Lemaitre, Douglas Arnold, Tal Arbel
      Pages 23-32

    5. Reg R-CNN: Lesion Detection and Grading Under Noisy Labels

      • Gregor N. Ramien, Paul F. Jaeger, Simon A. A. Kohl, Klaus H. Maier-Hein
      Pages 33-41

    6. Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimation

      • Mikael Agn, Koen Van Leemput
      Pages 42-51

    7. Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classification

      • Pieter Van Molle, Tim Verbelen, Cedric De Boom, Bert Vankeirsbilck, Jonas De Vylder, Bart Diricx et al.
      Pages 52-61

  3. UNSURE 2019: Domain Shift Robustness

    1. Front Matter

      Pages 63-63
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    2. A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data

      • Min Zhang, Kevin H. Leung, Zili Ma, Jin Wen, Gopal Avinash
      Pages 65-74

    3. Out of Distribution Detection for Intra-operative Functional Imaging

      • Tim J. Adler, Leonardo Ayala, Lynton Ardizzone, Hannes G. Kenngott, Anant Vemuri, Beat P. Müller-Stich et al.
      Pages 75-82

  4. CLIP 2019

    1. Front Matter

      Pages 83-83
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    2. A Clinical Measuring Platform for Building the Bridge Across the Quantification of Pathological N-Cells in Medical Imaging for Studies of Disease

      • Peifang Guo
      Pages 85-93

    3. Spatiotemporal Statistical Model of Anatomical Landmarks on a Human Embryonic Brain

      • Aoi Shinjo, Atsushi Saito, Tetsuya Takakuwa, Shigehito Yamada, Hidekata Hontani, Hiroshi Matsuzoe et al.
      Pages 94-103

    4. Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Ileus Diagnosis

      • Hirohisa Oda, Kohei Nishio, Takayuki Kitasaka, Benjamin Villard, Hizuru Amano, Kosuke Chiba et al.
      Pages 104-114

    5. Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates

      • Ayushi Sinha, Xingtong Liu, Masaru Ishii, Gregory D. Hager, Russell H. Taylor
      Pages 115-124

    6. Synthesis of Medical Images Using GANs

      • Luise Middel, Christoph Palm, Marius Erdt
      Pages 125-134

    7. DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation

      • Shuting Liu, Baochang Zhang, Xi Li, Yiqing Liu, Mengying Hu, Tian Guan et al.
      Pages 135-145

    8. Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging

      • Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Hangfan Liu et al.
      Pages 146-154

    9. Data Augmentation from Sketch

      • Debora Gil, Antonio Esteban-Lansaque, Sebastian Stefaniga, Mihail Gaianu, Carles Sanchez
      Pages 155-162
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  1. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

Keywords

About this book

This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world.

CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data. 

Editors and Affiliations

  • Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  • University College London, London, UK

    Ryutaro Tanno

  • Fraunhofer Singapore, Nanyang Technological University, Singapore, Singapore

    Marius Erdt

  • McGill University, Montreal, Canada

    Tal Arbel

  • ETH Zürich, Zürich, Switzerland

    Christian Baumgartner

  • Massachusetts Institute of Technology, Harvard Medical School, Cambridge, USA

    Adrian Dalca

  • University College London, King's College London, London, UK

    Carole H. Sudre

  • Harvard Medical School, Boston, USA

    William M. Wells

  • Aachen University of Applied Sciences, Aachen, Germany

    Klaus Drechsler

  • Children’s National Healthcare System, Washington, D.C., USA

    Marius George Linguraru

  • Fraunhofer IGD, Darmstadt, Germany

    Cristina Oyarzun Laura, Stefan Wesarg

  • Children's National Healthcare System, Washington, D.C., USA

    Raj Shekhar

  • ICREA - Universitat Pompeu Fabra, Barcelona, Spain

    Miguel Ángel González Ballester

Bibliographic Information

  • Book Title: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

  • Book Subtitle: First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Editors: Hayit Greenspan, Ryutaro Tanno, Marius Erdt, Tal Arbel, Christian Baumgartner, Adrian Dalca, Carole H. Sudre, William M. Wells, Klaus Drechsler, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, … Miguel Ángel González Ballester

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-32689-0

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-32688-3Published: 11 October 2019

  • eBook ISBN: 978-3-030-32689-0Published: 10 October 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVII, 192

  • Number of Illustrations: 7 b/w illustrations, 76 illustrations in colour

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

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