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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

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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).

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Correspondence to Kerstin Ritter .

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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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