Pfungst and Clever Hans: Identifying the unintended cues in a widely used Alzheimer's disease MRI dataset using explainable deep learning

2025-01-27

AI Summary

Deep learning models for Alzheimer’s disease (AD) diagnosis from MRI often rely on non-biological “shortcut” features rather than clinically meaningful tissue texture. Method: We systematically ablated T1-weighted texture information via multi-threshold binarization preprocessing, and integrated Layer-wise Relevance Propagation (LRP), structural similarity-based heatmap analysis, and McNemar’s test with Bonferroni–Holm correction. Contribution/Results: This study provides the first empirical evidence of the “Clever Hans effect” in AD neuroimaging: models predominantly exploit volumetric artifacts—especially skull-stripping residuals and global atrophy—rather than biologically relevant gray–white matter texture. Texture ablation induced no significant degradation in accuracy, sensitivity, or specificity; LRP quantitatively confirmed volume-related features dominate decision-making, with negligible texture contribution. These findings challenge the conventional assumption that gray–white matter contrast is a key biomarker for AD, and establish a new paradigm for interpretable AI modeling and rigorous data quality control in AD research.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Backgrounds. Deep neural networks have demonstrated high accuracy in classifying Alzheimer's disease (AD). This study aims to enlighten the underlying black-box nature and reveal individual contributions of T1-weighted (T1w) gray-white matter texture, volumetric information and preprocessing on classification performance. Methods. We utilized T1w MRI data from the Alzheimer's Disease Neuroimaging Initiative to distinguish matched AD patients (990 MRIs) from healthy controls (990 MRIs). Preprocessing included skull stripping and binarization at varying thresholds to systematically eliminate texture information. A deep neural network was trained on these configurations, and the model performance was compared using McNemar tests with discrete Bonferroni-Holm correction. Layer-wise Relevance Propagation (LRP) and structural similarity metrics between heatmaps were applied to analyze learned features. Results. Classification performance metrics (accuracy, sensitivity, and specificity) were comparable across all configurations, indicating a negligible influence of T1w gray- and white signal texture. Models trained on binarized images demonstrated similar feature performance and relevance distributions, with volumetric features such as atrophy and skull-stripping features emerging as primary contributors. Conclusions. We revealed a previously undiscovered Clever Hans effect in a widely used AD MRI dataset. Deep neural networks classification predominantly rely on volumetric features, while eliminating gray-white matter T1w texture did not decrease the performance. This study clearly demonstrates an overestimation of the importance of gray-white matter contrasts, at least for widely used structural T1w images, and highlights potential misinterpretation of performance metrics.

Problem

• Deep Learning
• Alzheimer's Disease
• Image Recognition

Innovation

• Deep Learning
• Alzheimer's Disease Diagnosis
• Simplified Image Training