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International Workshop on Multimodal Learning for Clinical Decision Support

International Workshop on Interpretability of Machine Intelligence in Medical Image Computing

ML-CDS 2019, IMIMIC 2019: Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support pp 3–11Cite as

Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer’s Disease Classification

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11797)

Abstract

Attribution methods are an easy to use tool for investigating and validating machine learning models. Multiple methods have been suggested in the literature and it is not yet clear which method is most suitable for a given task. In this study, we tested the robustness of four attribution methods, namely gradient * input, guided backpropagation, layer-wise relevance propagation and occlusion, for the task of Alzheimer’s disease classification. We have repeatedly trained a convolutional neural network (CNN) with identical training settings in order to separate structural MRI data of patients with Alzheimer’s disease and healthy controls. Afterwards, we produced attribution maps for each subject in the test data and quantitatively compared them across models and attribution methods. We show that visual comparison is not sufficient and that some widely used attribution methods produce highly inconsistent outcomes.

Keywords

  • Machine learning
  • Convolutional neural networks
  • MRI
  • Explainability
  • Robustness
  • Attribution methods
  • Alzheimer’s disease

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Notes

  1. 1.

    http://adni.loni.usc.edu/.

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Funding

We acknowledge support from the German Research Foundation (DFG, 389563835), the Manfred and Ursula-Müller Stiftung, the Brain & Behavior Research Foundation (NARSAD grant, USA), the Deutsche Multiple Sklerose Gesellschaft (DMSG) Bundesverband e.V. and Charité – Universitätsmedizin Berlin (Rahel-Hirsch scholarship).

Author information

Authors and Affiliations

  1. Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), 10117, Berlin, Germany

    Fabian Eitel & Kerstin Ritter

  2. Berlin Center for Advanced Neuroimaging, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), 10117, Berlin, Germany

    Fabian Eitel & Kerstin Ritter

  3. Bernstein Center for Computational Neuroscience, 10117, Berlin, Germany

    Fabian Eitel & Kerstin Ritter

Authors
  1. Fabian Eitel
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  2. Kerstin Ritter
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for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Corresponding author

Correspondence to Kerstin Ritter .

Editor information

Editors and Affiliations

  1. Tokyo Institute of Technology, Yokohama, Japan

    Kenji Suzuki

  2. University of Bern, Bern, Switzerland

    Prof. Dr. Mauricio Reyes

  3. IBM Research - Almaden, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  4. ETH Zurich, Zürich, Zürich, Germany

    Prof. Dr. Ender Konukoglu

  5. Imperial College London, London, UK

    Dr. Ben Glocker

  6. University Hospital of Bern, Bern, Switzerland

    Prof. Dr. med. Roland Wiest

  7. IBM Research - Almaden, San Jose, CA, USA

    Yaniv Gur

  8. Tel Aviv University, Ramat Aviv, Israel

    Hayit Greenspan

  9. Case Western Reserve University, Cleveland, OH, USA

    Dr. Anant Madabhushi

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Eitel, F., Ritter, K., for the Alzheimer’s Disease Neuroimaging Initiative (ADNI). (2019). Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer’s Disease Classification. In: , et al. Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support. ML-CDS IMIMIC 2019 2019. Lecture Notes in Computer Science(), vol 11797. Springer, Cham. https://doi.org/10.1007/978-3-030-33850-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-33850-3_1

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