Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification

TL;DR

This study reveals that deep learning models for Alzheimer's classification from MRI rely heavily on skull-stripping artifacts, leading to shortcut learning that may compromise interpretability and robustness.

Contribution

It systematically investigates how preprocessing, especially skull-stripping, influences model decisions, highlighting the unintended reliance on artifacts rather than genuine disease markers.

Findings

Models maintain accuracy despite changes in image content.

Volumetric features, especially brain contours, are key cues.

Skull-stripping artifacts act as shortcut cues for classification.

Abstract

Objectives: High classification accuracy of Alzheimer's disease (AD) from structural MRI has been achieved using deep neural networks, yet the specific image features contributing to these decisions remain unclear. In this study, the contributions of T1-weighted (T1w) gray-white matter texture, volumetric information, and preprocessing -- particularly skull-stripping -- were systematically assessed. Methods: A dataset of 990 matched T1w MRIs from AD patients and cognitively normal controls from the ADNI database were used. Preprocessing was varied through skull-stripping and intensity binarization to isolate texture and shape contributions. A 3D convolutional neural network was trained on each configuration, and classification performance was compared using exact McNemar tests with discrete Bonferroni-Holm correction. Feature relevance was analyzed using Layer-wise Relevance Propagation, image similarity metrics, and spectral clustering of relevance maps. Results: Despite substantial differences in image content, classification accuracy, sensitivity, and specificity remained stable across preprocessing conditions. Models trained on binarized images preserved performance, indicating minimal reliance on gray-white matter texture. Instead, volumetric features -- particularly brain contours introduced through skull-stripping -- were consistently used by the models. Conclusions: This behavior reflects a shortcut learning phenomenon, where preprocessing artifacts act as potentially unintended cues. The resulting Clever Hans effect emphasizes the critical importance of interpretability tools to reveal hidden biases and to ensure robust and trustworthy deep learning in medical imaging.