Uncertainty for Safe Utilization of Machine Learning in Medical Imaging: 6th International Workshop, UNSURE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings | SpringerLink
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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

6th International Workshop, UNSURE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings

  • Conference proceedings
  • © 2025

Overview

Editors:
  1. Carole H. Sudre

    1. University College London, London, UK

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  2. Raghav Mehta

    1. Imperial College London, London, UK

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  3. Cheng Ouyang

    1. Oxford University, Oxford, UK

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  4. Chen Qin

    1. Imperial College London, London, UK

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  5. Marianne Rakic

    1. Massachusetts Institute of Technology, Cambridge, USA

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  6. William M. Wells

    1. Harvard Medical School, Boston, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15167)

Included in the following conference series:

Conference proceedings info: UNSURE 2024.

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About this book

This book constitutes the refereed proceedings of the 6th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, on October 10, 2024.

The 20 full papers presented in this book were carefully reviewed and selected from 28 submissions. They are organized in the following topical sections: annotation uncertainty; clinical implementation of uncertainty modelling and risk management in clinical pipelines; out of distribution and domain shift identification and management; uncertainty modelling and estimation.

Keywords

Table of contents (20 papers)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Annotation Uncertainty

    1. Front Matter

      Pages 1-1
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    2. Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detection

      • Deeksha M. Shama, Archana Venkataraman
      Pages 3-13

    3. Active Learning for Scribble-Based Diffusion MRI Segmentation

      • Jonathan Lennartz, Golo Pohl, Thomas Schultz
      Pages 14-22

    4. FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection

      • Simon Gutwein, Martin Kampel, Sabine Taschner-Mandl, Roxane Licandro
      Pages 23-33

    5. Diagnose with Uncertainty Awareness: Diagnostic Uncertainty Encoding Framework for Radiology Report Generation

      • Sixing Yan, Haiyan Yin, Ivor W. Tsang, William K. Cheung
      Pages 34-44

  3. Clinical Implementation of Uncertainty Modelling and Risk Management in Clinical Pipelines

    1. Front Matter

      Pages 45-45
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    2. Making Deep Learning Models Clinically Useful - Improving Diagnostic Confidence in Inherited Retinal Disease with Conformal Prediction

      • Biraja Ghoshal, William Woof, Bernardo Mendes, Saoud Al-Khuzaei, Thales Antonio Cabral De Guimaraes, Malena Daich Varela et al.
      Pages 47-58

    3. GUARDIAN: Guarding Against Uncertainty and Adversarial Risks in Robot-Assisted Surgeries

      • Ufaq Khan, Umair Nawaz, Tooba T. Sheikh, Asif Hanif, Mohammad Yaqub
      Pages 59-69

    4. Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components

      • Hermione Warr, Yasin Ibrahim, Daniel R. McGowan, Konstantinos Kamnitsas
      Pages 70-80

    5. Conformal Performance Range Prediction for Segmentation Output Quality Control

      • Anna M. Wundram, Paul Fischer, Michael Mühlebach, Lisa M. Koch, Christian F. Baumgartner
      Pages 81-91

    6. Holistic Consistency for Subject-Level Segmentation Quality Assessment in Medical Image Segmentation

      • Yizhe Zhang, Tao Zhou, Qiang Chen, Qi Dou, Shuo Wang
      Pages 92-102

  4. Out of Distribution and Domain Shift Identification and Management

    1. Front Matter

      Pages 103-103
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    2. CROCODILE: Causality Aids RObustness via COntrastive DIsentangled LEarning

      • Gianluca Carloni, Sotirios A. Tsaftaris, Sara Colantonio
      Pages 105-116

    3. Image-Conditioned Diffusion Models for Medical Anomaly Detection

      • Matthew Baugh, Hadrien Reynaud, Sergio Naval Marimont, Sarah Cechnicka, Johanna P. Müller, Giacomo Tarroni et al.
      Pages 117-127

    4. Information Bottleneck-Based Feature Weighting for Enhanced Medical Image Out-of-Distribution Detection

      • Brayden Schott, Žan Klaneček, Alison Deatsch, Victor Santoro-Fernandes, Thomas Francken, Scott Perlman et al.
      Pages 128-137

    5. Beyond Heatmaps: A Comparative Analysis of Metrics for Anomaly Localization in Medical Images

      • David Zimmerer, Klaus Maier-Hein
      Pages 138-148

    6. Typicality Excels Likelihood for Unsupervised Out-of-Distribution Detection in Medical Imaging

      • Lemar Abdi, M. M. Amaan Valiuddin, Christiaan G. A. Viviers, Peter H. N. de With, Fons van der Sommen
      Pages 149-159

    7. Evaluating Reliability in Medical DNNs: A Critical Analysis of Feature and Confidence-Based OOD Detection

      • Harry Anthony, Konstantinos Kamnitsas
      Pages 160-170

    8. Uncertainty-Aware Vision Transformers for Medical Image Analysis

      • Franciskus Xaverius Erick, Mina Rezaei, Johanna Paula Müller, Bernhard Kainz
      Pages 171-180
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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

  • Imperial College London, London, UK

    Raghav Mehta, Chen Qin

  • Oxford University, Oxford, UK

    Cheng Ouyang

  • Massachusetts Institute of Technology, Cambridge, USA

    Marianne Rakic

  • Harvard Medical School, Boston, USA

    William M. Wells

Bibliographic Information

  • Book Title: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

  • Book Subtitle: 6th International Workshop, UNSURE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings

  • Editors: Carole H. Sudre, Raghav Mehta, Cheng Ouyang, Chen Qin, Marianne Rakic, William M. Wells

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-73158-7

  • Publisher: Springer Cham

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

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025

  • Softcover ISBN: 978-3-031-73157-0Published: 02 October 2024

  • eBook ISBN: 978-3-031-73158-7Published: 02 October 2024

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 226

  • Number of Illustrations: 6 b/w illustrations, 61 illustrations in colour

  • Topics: Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence, Computing Milieux, Computer Applications

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