AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans

This study presents an innovative method for Alzheimer's disease diagnosisusing 3D MRI designed to enhance the explainability of model decisions. Ourapproach adopts a soft attention mechanism, enabling 2D CNNs to extractvolumetric representations. At the same time, the importance of each slice indecision-making is learned, allowing the generation of a voxel-level attentionmap to produce an explainable MRI. To test our method and ensure thereproducibility of our results, we chose a standardized collection of MRI datafrom the Alzheimer's Disease Neuroimaging Initiative (ADNI). On this dataset,our method significantly outperforms state-of-the-art methods in (i)distinguishing AD from cognitive normal (CN) with an accuracy of 0.856 andMatthew's correlation coefficient (MCC) of 0.712, representing improvements of2.4% and 5.3% respectively over the second-best, and (ii) in the prognostictask of discerning stable from progressive mild cognitive impairment (MCI) withan accuracy of 0.725 and MCC of 0.443, showing improvements of 10.2% and 20.5%respectively over the second-best. We achieved this prognostic result byadopting a double transfer learning strategy, which enhanced sensitivity tomorphological changes and facilitated early-stage AD detection. Withvoxel-level precision, our method identified which specific areas are beingpaid attention to, identifying these predominant brain regions: thehippocampus, the amygdala, the parahippocampal, and the inferior lateralventricles. All these areas are clinically associated with AD development.Furthermore, our approach consistently found the same AD-related areas acrossdifferent cross-validation folds, proving its robustness and precision inhighlighting areas that align closely with known pathological markers of thedisease.