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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 102–111Cite as

Out of Distribution Detection for Medical Images

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

Abstract

Neural network architectures behave in unpredictable ways when testing on inputs which do not resemble their training data. It is valuable to detect any out-of-distribution (OOD) inputs to make any overseers aware of the limitations of the model’s output. To address this need, a large number of methods for detecting OOD inputs have been proposed and tested on small datasets such as CIFAR10, SVHN, or LSUN. The purpose of this study is to determine the effectiveness of different methods for OOD detection on the domain of medical images. We investigate three common OOD detection methods (Maximum Softmax Probability, Confidence Branch, and Outlier Exposure) and report their effectiveness on widely used medical image datasets. We find that OOD detection metrics are volatile and can have large changes in performance in a short amount of training steps. Moreover, we also observe that OOD detection is sensitive to the choice of hyperparameters. Our code is reproducible at this link (https://github.com/oliverzhang42/ood_medical_images).

Keywords

  • Out of distribution detection
  • Medical image processing
  • Deep learning

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Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Oliver Zhang, Jean-Benoit Delbrouck & Daniel L. Rubin

Authors
  1. Oliver Zhang
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  2. Jean-Benoit Delbrouck
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  3. Daniel L. Rubin
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Corresponding author

Correspondence to Oliver Zhang .

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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Supplementary material 1 (pdf 298 KB)

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Zhang, O., Delbrouck, JB., Rubin, D.L. (2021). Out of Distribution Detection for Medical Images. 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_10

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  • DOI: https://doi.org/10.1007/978-3-030-87735-4_10

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