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Owing to its pristine soft-tissue contrast and high resolution, structuralmagnetic resonance imaging (MRI) is widely applied in neurology, making it avaluable data source for image-based machine learning (ML) and deep learningapplications. The physical nature of MRI acquisition and reconstruction,however, causes variations in image intensity, resolution, and signal-to-noiseratio. Since ML models are sensitive to such variations, performance onout-of-distribution data, which is inherent to the setting of a deployedhealthcare ML application, typically drops below acceptable levels. We proposean interpretability aware adversarial training regime to improve robustnessagainst out-of-distribution samples originating from different MRI hardware.The approach is applied to 1.5T and 3T MRIs obtained from the Alzheimer'sDisease Neuroimaging Initiative database. We present preliminary resultsshowing promising performance on out-of-distribution samples.

Interpretability Aware Model Training to Improve Robustness against  Out-of-Distribution Magnetic Resonance Images in Alzheimer's Disease  Classification