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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

4th International Workshop, UNSURE 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings

  • Conference proceedings
  • © 2022

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Editors:
  1. Carole H. Sudre

    1. University College London, London, UK

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  2. Christian F. Baumgartner

    1. University of Tübingen, Tübingen, Germany

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  3. Adrian Dalca

    1. Massachusetts General Hospital, Charlestown, USA

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  4. Chen Qin

    1. Imperial College London, London, UK

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  5. Ryutaro Tanno

    1. Google DeepMind, London, UK

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  6. Koen Van Leemput

    1. Technical University of Denmark, Kongens Lyngby, Denmark

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  7. William M. Wells III

    1. Harvard Medical School, Boston, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13563)

Included in the following conference series:

Conference proceedings info: UNSURE 2022.

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About this book

This book constitutes the refereed proceedings of the Fourth Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2022, held in conjunction with MICCAI 2022. The conference was hybrid event held from Singapore. For this workshop, 13 papers from 22 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.

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Keywords

Table of contents (13 papers)

  1. Front Matter

    Pages i-x
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  2. Uncertainty Modelling

    1. Front Matter

      Pages 1-1
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    2. MOrphologically-Aware Jaccard-Based ITerative Optimization (MOJITO) for Consensus Segmentation

      • Dimitri Hamzaoui, Sarah Montagne, Raphaële Renard-Penna, Nicholas Ayache, Hervé Delingette
      Pages 3-13

    3. Quantification of Predictive Uncertainty via Inference-Time Sampling

      • Katarína Tóthová, Ľubor Ladický, Daniel Thul, Marc Pollefeys, Ender Konukoglu
      Pages 14-25

    4. Uncertainty Categories in Medical Image Segmentation: A Study of Source-Related Diversity

      • Luke Whitbread, Mark Jenkinson
      Pages 26-35

    5. On the Pitfalls of Entropy-Based Uncertainty for Multi-class Semi-supervised Segmentation

      • Martin Van Waerebeke, Gregory Lodygensky, Jose Dolz
      Pages 36-46

    6. What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?

      • Gangin Park, Chunsan Hong, Bohyung Kim, Won Hwa Kim
      Pages 47-56

  3. Uncertainty Calibration

    1. Front Matter

      Pages 57-57
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    2. Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation

      • Cheng Ouyang, Shuo Wang, Chen Chen, Zeju Li, Wenjia Bai, Bernhard Kainz et al.
      Pages 59-69

    3. Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty

      • Prerak Mody, Nicolas F. Chaves-de-Plaza, Klaus Hildebrandt, Marius Staring
      Pages 70-79

    4. Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty

      • Hariharan Ravishankar, Rohan Patil, Deepa Anand, Vanika Singhal, Utkarsh Agrawal, Rahul Venkataramani et al.
      Pages 80-88

    5. Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopathology Datasets

      • Jacob Carse, Andres Alvarez Olmo, Stephen McKenna
      Pages 89-99

  4. Annotation Uncertainty and Out of Distribution Management

    1. Front Matter

      Pages 101-101
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    2. nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods

      • Matthew Baugh, Jeremy Tan, Athanasios Vlontzos, Johanna P. Müller, Bernhard Kainz
      Pages 103-112

    3. Generalized Probabilistic U-Net for Medical Image Segementation

      • Ishaan Bhat, Josien P. W. Pluim, Hugo J. Kuijf
      Pages 113-124

    4. Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-task TransUNet

      • Parinaz Roshanzamir, Hassan Rivaz, Joshua Ahn, Hamza Mirza, Neda Naghdi, Meagan Anstruther et al.
      Pages 125-134

    5. Information Gain Sampling for Active Learning in Medical Image Classification

      • Raghav Mehta, Changjian Shui, Brennan Nichyporuk, Tal Arbel
      Pages 135-145

  5. Back Matter

    Pages 147-147
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Other volumes

  1. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

Editors and Affiliations

  • University College London, London, UK

    Carole H. Sudre

  • University of Tübingen, Tübingen, Germany

    Christian F. Baumgartner

  • Massachusetts General Hospital, Charlestown, USA

    Adrian Dalca

  • Imperial College London, London, UK

    Chen Qin

  • Google DeepMind, London, UK

    Ryutaro Tanno

  • Technical University of Denmark, Kongens Lyngby, Denmark

    Koen Van Leemput

  • Harvard Medical School, Boston, USA

    William M. Wells III

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