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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Carole H. Sudre

    1. University College London, London, UK

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  2. Hamid Fehri

    1. University of Oxford, Oxford, UK

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  3. Tal Arbel

    1. McGill University, Montreal, Canada

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  4. Christian F. Baumgartner

    1. ETH Zurich, Zürich, Switzerland

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  5. Adrian Dalca

    1. Massachusetts General Hospital, Charlestown, USA

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  6. Ryutaro Tanno

    1. University College London, London, UK

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  7. Koen Van Leemput

    1. Technical University of Denmark, Kongens Lyngby, Denmark

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  8. William M. Wells

    1. Harvard Medical School, Boston, USA

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  9. Aristeidis Sotiras

    1. Washington University School of Medicine, St. Louis, USA

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  10. Bartlomiej Papiez

    1. University of Oxford, Oxford, UK

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  11. Enzo Ferrante

    1. Ciudad Universitaria UNL, Santa Fe, Argentina

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  12. Sarah Parisot

    1. Huawei Noah’s Ark Lab, London, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12443)

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

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Table of contents (21 papers)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. UNSURE 2020

    1. Front Matter

      Pages 1-1
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    2. Image Registration via Stochastic Gradient Markov Chain Monte Carlo

      • Daniel Grzech, Bernhard Kainz, Ben Glocker, Loïc le Folgoc
      Pages 3-12

    3. RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation

      • Marc Gantenbein, Ertunc Erdil, Ender Konukoglu
      Pages 13-22

    4. Hierarchical Brain Parcellation with Uncertainty

      • Mark S. Graham, Carole H. Sudre, Thomas Varsavsky, Petru-Daniel Tudosiu, Parashkev Nachev, Sebastien Ourselin et al.
      Pages 23-31

    5. Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation

      • Robin Camarasa, Daniel Bos, Jeroen Hendrikse, Paul Nederkoorn, Eline Kooi, Aad van der Lugt et al.
      Pages 32-41

    6. Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps

      • Christian Payer, Martin Urschler, Horst Bischof, Darko Štern
      Pages 42-51

    7. Weight Averaging Impact on the Uncertainty of Retinal Artery-Venous Segmentation

      • Markus Lindén, Azat Garifullin, Lasse Lensu
      Pages 52-60

    8. Improving Pathological Distribution Measurements with Bayesian Uncertainty

      • Ka Ho Tam, Korsuk Sirinukunwattana, Maria F. Soares, Maria Kaisar, Rutger Ploeg, Jens Rittscher
      Pages 61-70

    9. Improving Reliability of Clinical Models Using Prediction Calibration

      • Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan, Prasanna Sattigeri
      Pages 71-80

    10. Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior

      • Max-Heinrich Laves, Malte Tölle, Tobias Ortmaier
      Pages 81-96

    11. Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability

      • Arunkumar Kannan, Antony Hodgson, Kishore Mulpuri, Rafeef Garbi
      Pages 97-105

  3. GRAIL 2020

    1. Front Matter

      Pages 107-107
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    2. Clustering-Based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates

      • Uğur Demir, Mohammed Amine Gharsallaoui, Islem Rekik
      Pages 109-120

    3. Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences

      • Xiaodan Xing, Lili Jin, Qinfeng Li, Lei Chen, Zhong Xue, Ziwen Peng et al.
      Pages 121-130

    4. Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders

      • Rui Sherry Shen, Jacob A. Alappatt, Drew Parker, Junghoon Kim, Ragini Verma, Yusuf Osmanlıoğlu
      Pages 131-141

    5. Multi-scale Profiling of Brain Multigraphs by Eigen-Based Cross-diffusion and Heat Tracing for Brain State Profiling

      • Mustafa Sağlam, Islem Rekik
      Pages 142-151

    6. Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation

      • Karthik Gopinath, Christian Desrosiers, Herve Lombaert
      Pages 152-163

    7. Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth

      • Hassna Irzan, Lucas Fidon, Tom Vercauteren, Sebastien Ourselin, Neil Marlow, Andrew Melbourne
      Pages 164-173

    8. Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface

      • Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loic Le Folgoc et al.
      Pages 174-186
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  1. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

Keywords

About this book

This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic.

For UNSURE 2020, 10 papers from 18 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.

GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.

Editors and Affiliations

  • University College London, London, UK

    Carole H. Sudre, Ryutaro Tanno

  • University of Oxford, Oxford, UK

    Hamid Fehri, Bartlomiej Papiez

  • McGill University, Montreal, Canada

    Tal Arbel

  • ETH Zurich, Zürich, Switzerland

    Christian F. Baumgartner

  • Massachusetts General Hospital, Charlestown, USA

    Adrian Dalca

  • Technical University of Denmark, Kongens Lyngby, Denmark

    Koen Van Leemput

  • Harvard Medical School, Boston, USA

    William M. Wells

  • Washington University School of Medicine, St. Louis, USA

    Aristeidis Sotiras

  • Ciudad Universitaria UNL, Santa Fe, Argentina

    Enzo Ferrante

  • Huawei Noah’s Ark Lab, London, UK

    Sarah Parisot

Bibliographic Information

  • Book Title: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

  • Book Subtitle: Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings

  • Editors: Carole H. Sudre, Hamid Fehri, Tal Arbel, Christian F. Baumgartner, Adrian Dalca, Ryutaro Tanno, Koen Van Leemput, William M. Wells, Aristeidis Sotiras, Bartlomiej Papiez, Enzo Ferrante, … Sarah Parisot

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-60365-6

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Softcover ISBN: 978-3-030-60364-9Published: 06 October 2020

  • eBook ISBN: 978-3-030-60365-6Published: 05 October 2020

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVII, 222

  • Number of Illustrations: 9 b/w illustrations, 76 illustrations in colour

  • Topics: Artificial Intelligence, Pattern Recognition, Image Processing and Computer Vision, Computer Appl. in Social and Behavioral Sciences

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