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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis

3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Carole H. Sudre

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

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  2. Roxane Licandro

    1. Medical University of Vienna and TU Wien, Vienna, Austria

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  3. Christian Baumgartner

    1. University of Tübingen, Tübingen, Germany

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  4. Andrew Melbourne

    1. King's College London, London, UK

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  5. Adrian Dalca

    1. Massachusetts General Hospital, Harvard Medical School, MIT, Cambridge, USA

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  6. Jana Hutter

    1. King's College London, London, UK

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  7. Ryutaro Tanno

    1. Microsoft Research/University College London, London, UK

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  8. Esra Abaci Turk

    1. Boston Children's Hospital, Boston, USA

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  9. Koen Van Leemput

    1. Technical University Denmark, Kongens Lyngby, Denmark

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  10. Jordina Torrents Barrena

    1. Hewlett Packard, Barcelona, Spain

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  11. William M. Wells

    1. Harvard Medical School/Brigham and Women's Hospital, Boston, USA

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  12. Christopher Macgowan

    1. The Hospital For Sick Children, University of Toronto, Toronto, Canada

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12959)

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

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Table of contents (27 papers)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. UNSURE 2021: Uncertainty Estimation and Modelling and Annotation Uncertainty

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Modal Uncertainty Estimation for Medical Imaging Based Diagnosis

      • Di Qiu, Lok Ming Lui
      Pages 3-13

    3. Accurate Simulation of Operating System Updates in Neuroimaging Using Monte-Carlo Arithmetic

      • Ali Salari, Yohan Chatelain, Gregory Kiar, Tristan Glatard
      Pages 14-23

    4. Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR

      • Tewodros Weldebirhan Arega, Stéphanie Bricq, Fabrice Meriaudeau
      Pages 24-33

    5. Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning

      • Sora Iwamoto, Bisser Raytchev, Toru Tamaki, Kazufumi Kaneda
      Pages 34-43

    6. Unpaired MR Image Homogenisation by Disentangled Representations and Its Uncertainty

      • Hongwei Li, Sunita Gopal, Anjany Sekuboyina, Jianguo Zhang, Chen Niu, Carolin Pirkl et al.
      Pages 44-53

    7. Uncertainty-Aware Deep Learning Based Deformable Registration

      • Irina Grigorescu, Alena Uus, Daan Christiaens, Lucilio Cordero-Grande, Jana Hutter, Dafnis Batalle et al.
      Pages 54-63

    8. Monte Carlo Concrete DropPath for Epistemic Uncertainty Estimation in Brain Tumor Segmentation

      • Natalia Khanzhina, Maxim Kashirin, Andrey Filchenkov
      Pages 64-74

    9. Improving Aleatoric Uncertainty Quantification in Multi-annotated Medical Image Segmentation with Normalizing Flows

      • M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Ruud J. G. van Sloun, Peter H. N. de With, Fons van der Sommen
      Pages 75-88

  3. UNSURE 2021: Domain Shift Robustness and Risk Management in Clinical Pipelines

    1. Front Matter

      Pages 89-89
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    2. Task-Agnostic Out-of-Distribution Detection Using Kernel Density Estimation

      • Ertunc Erdil, Krishna Chaitanya, Neerav Karani, Ender Konukoglu
      Pages 91-101

    3. Out of Distribution Detection for Medical Images

      • Oliver Zhang, Jean-Benoit Delbrouck, Daniel L. Rubin
      Pages 102-111

    4. Robust Selective Classification of Skin Lesions with Asymmetric Costs

      • Jacob Carse, Tamás Süveges, Stephen Hogg, Emanuele Trucco, Charlotte Proby, Colin Fleming et al.
      Pages 112-121

    5. Confidence-Based Out-of-Distribution Detection: A Comparative Study and Analysis

      • Christoph Berger, Magdalini Paschali, Ben Glocker, Konstantinos Kamnitsas
      Pages 122-132

    6. Novel Disease Detection Using Ensembles with Regularized Disagreement

      • Alexandru Țifrea, Eric Stavarache, Fanny Yang
      Pages 133-144

  4. PIPPI 2021

    1. Front Matter

      Pages 145-145
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    2. Automatic Placenta Abnormality Detection Using Convolutional Neural Networks on Ultrasound Texture

      • Zoe Hu, Ricky Hu, Ryan Yan, Chantal Mayer, Robert N. Rohling, Rohit Singla
      Pages 147-156

    3. Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction

      • Hélène Lajous, Tom Hilbert, Christopher W. Roy, Sébastien Tourbier, Priscille de Dumast, Yasser Alemán-Gómez et al.
      Pages 157-167

    4. Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI

      • Alena Uus, Jacqueline Matthew, Irina Grigorescu, Samuel Jupp, Lucilio Cordero Grande, Anthony Price et al.
      Pages 168-178
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  1. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis

Keywords

About this book

This book constitutes the refereed proceedings of the Third International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 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.

PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.

Editors and Affiliations

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

    Carole H. Sudre

  • Medical University of Vienna and TU Wien, Vienna, Austria

    Roxane Licandro

  • University of Tübingen, Tübingen, Germany

    Christian Baumgartner

  • King's College London, London, UK

    Andrew Melbourne, Jana Hutter

  • Massachusetts General Hospital, Harvard Medical School, MIT, Cambridge, USA

    Adrian Dalca

  • Microsoft Research/University College London, London, UK

    Ryutaro Tanno

  • Boston Children's Hospital, Boston, USA

    Esra Abaci Turk

  • Technical University Denmark, Kongens Lyngby, Denmark

    Koen Van Leemput

  • Hewlett Packard, Barcelona, Spain

    Jordina Torrents Barrena

  • Harvard Medical School/Brigham and Women's Hospital, Boston, USA

    William M. Wells

  • The Hospital For Sick Children, University of Toronto, Toronto, Canada

    Christopher Macgowan

Bibliographic Information

  • Book Title: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis

  • Book Subtitle: 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

  • Editors: Carole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, … Christopher Macgowan

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-87735-4

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2021

  • Softcover ISBN: 978-3-030-87734-7Published: 01 October 2021

  • eBook ISBN: 978-3-030-87735-4Published: 30 September 2021

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XIII, 296

  • Number of Illustrations: 9 b/w illustrations, 103 illustrations in colour

  • Topics: Artificial Intelligence, Image Processing and Computer Vision, Computational Biology/Bioinformatics, Pattern Recognition

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