Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification

TL;DR

This study evaluates the robustness of four attribution methods in CNNs for MRI-based Alzheimer's disease classification, revealing inconsistencies that question their reliability for model interpretation.

Contribution

It systematically compares four attribution methods' robustness in a CNN for Alzheimer's classification, highlighting their varying reliability and the need for careful method selection.

Findings

Some attribution methods produce highly inconsistent results.

Visual inspection alone is insufficient to assess attribution reliability.

Certain widely used methods may not be suitable for clinical interpretation.

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.