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International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

International Workshop on Preterm, Perinatal and Paediatric Image Analysis

UNSURE 2021, PIPPI 2021: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis pp 3–13Cite as

Modal Uncertainty Estimation for Medical Imaging Based Diagnosis

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

Abstract

Medical image based diagnosis is constantly faced with uncertainties. In an ambiguous scenario, different experts will reach different conclusions from their initial assumptions. It is thus important for machine learning models to be capable of proposing different plausible predictions, along with meaningful uncertainty measures. In this work we propose such a novel learning-based framework, named modal uncertainty estimation (MUE), to learn such one-to-many relationship with faithful uncertainty estimation in the medical image understanding tasks. Technically, MUE is based on conditional generative models, but it crucially uses a set of discrete latent variables, each representing a latent mode hypothesis that explains one type of input-output relationship. We justify the use of discrete latent variables by the multi-modal posterior collapse problem in the common conditional generative models. Consequently, MUE can estimate the uncertainty effectively. MUE demonstrates significantly more accurate uncertainty estimation for one-to-many relationship than the current state-of-the-art, and is more informative for practical use. We validate these points on both real and synthetic tasks.

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  • DOI: 10.1007/978-3-030-87735-4_1
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    https://github.com/sylqiu/modal_uncertainty.

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Author information

Authors and Affiliations

  1. The Chinese University of Hong Kong, Shatin, Hong Kong

    Di Qiu & Lok Ming Lui

Authors
  1. Di Qiu
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  2. Lok Ming Lui
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Editor information

Editors and Affiliations

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

    Carole H. Sudre

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

    Roxane Licandro

  3. University of Tübingen, Tübingen, Germany

    Christian Baumgartner

  4. King's College London, London, UK

    Prof. Dr. Andrew Melbourne

  5. Massachusetts General Hospital, Harvard Medical School, MIT, Cambridge, MA, USA

    Dr. Adrian Dalca

  6. King's College London, London, UK

    Dr. Jana Hutter

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

    Ryutaro Tanno

  8. Boston Children's Hospital, Boston, MA, USA

    Esra Abaci Turk

  9. Technical University Denmark, Kongens Lyngby, Denmark

    Koen Van Leemput

  10. Hewlett Packard, Barcelona, Spain

    Jordina Torrents Barrena

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

    William M. Wells

  12. The Hospital For Sick Children, University of Toronto, Toronto, ON, Canada

    Christopher Macgowan

1 Electronic supplementary material

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Qiu, D., Lui, L.M. (2021). Modal Uncertainty Estimation for Medical Imaging Based Diagnosis. In: , et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. UNSURE PIPPI 2021 2021. Lecture Notes in Computer Science(), vol 12959. Springer, Cham. https://doi.org/10.1007/978-3-030-87735-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-87735-4_1

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