Skull-stripping induces shortcut learning in MRI-based Alzheimer’s disease classification
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| Pubblicato in: | Insights into Imaging vol. 16, no. 1 (Dec 2025), p. 283 |
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Springer Nature B.V.
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| 100 | 1 | |a Tinauer, Christian |u Medical University of Graz, Department of Neurology, Graz, Austria (GRID:grid.11598.34) (ISNI:0000 0000 8988 2476) | |
| 245 | 1 | |a Skull-stripping induces shortcut learning in MRI-based Alzheimer’s disease classification | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a ObjectivesHigh 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.Materials and methodsA dataset of 990 matched T1w MRIs from AD patients and cognitively normal controls from the ADNI database was 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.ResultsDespite 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.ConclusionThis 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.Critical relevance statementWe investigated the mechanisms underlying deep learning-based disease classification using a widely utilized Alzheimer’s disease dataset, and our findings reveal a reliance on features induced through skull-stripping, highlighting the need for careful preprocessing to ensure clinically relevant and interpretable models.Key Points<list list-type="bullet"><list-item></list-item>Shortcut learning is induced by skull-stripping applied to T1-weighted MRIs.<list-item>Explainable deep learning and spectral clustering estimate the bias.</list-item><list-item>Highlights the importance of understanding the dataset, image preprocessing and deep learning model, for interpretation and validation.</list-item> | |
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Preprocessing | ||
| 653 | |a Alzheimer's disease | ||
| 653 | |a Bias | ||
| 653 | |a Configuration management | ||
| 653 | |a Classification | ||
| 653 | |a Clustering | ||
| 653 | |a Brain research | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Magnetic resonance imaging | ||
| 653 | |a Dementia | ||
| 653 | |a Medical imaging | ||
| 653 | |a Atrophy | ||
| 653 | |a Tissues | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Caregivers | ||
| 653 | |a Texture | ||
| 700 | 1 | |a Sackl, Maximilian |u Medical University of Graz, Department of Neurology, Graz, Austria (GRID:grid.11598.34) (ISNI:0000 0000 8988 2476) | |
| 700 | 1 | |a Stollberger, Rudolf |u Graz University of Technology, Institute of Biomedical Imaging, Graz, Austria (GRID:grid.410413.3) (ISNI:0000 0001 2294 748X); BioTechMed-Graz, Graz, Austria (GRID:grid.452216.6) | |
| 700 | 1 | |a Schmidt, Reinhold |u Medical University of Graz, Department of Neurology, Graz, Austria (GRID:grid.11598.34) (ISNI:0000 0000 8988 2476) | |
| 700 | 1 | |a Ropele, Stefan |u Medical University of Graz, Department of Neurology, Graz, Austria (GRID:grid.11598.34) (ISNI:0000 0000 8988 2476); BioTechMed-Graz, Graz, Austria (GRID:grid.452216.6) | |
| 700 | 1 | |a Langkammer, Christian |u Medical University of Graz, Department of Neurology, Graz, Austria (GRID:grid.11598.34) (ISNI:0000 0000 8988 2476); BioTechMed-Graz, Graz, Austria (GRID:grid.452216.6) | |
| 773 | 0 | |t Insights into Imaging |g vol. 16, no. 1 (Dec 2025), p. 283 | |
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