  This study presents an innovative method for Alzheimer's disease diagnosis
using 3D MRI designed to enhance the explainability of model decisions. Our
approach adopts a soft attention mechanism, enabling 2D CNNs to extract
volumetric representations. At the same time, the importance of each slice in
decision-making is learned, allowing the generation of a voxel-level attention
map to produces an explainable MRI. To test our method and ensure the
reproducibility of our results, we chose a standardized collection of MRI data
from 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 and
Matthew's correlation coefficient (MCC) of 0.712, representing improvements of
2.4\% and 5.3\% respectively over the second-best, and (ii) in the prognostic
task of discerning stable from progressive mild cognitive impairment (MCI) with
an 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 by
adopting a double transfer learning strategy, which enhanced sensitivity to
morphological changes and facilitated early-stage AD detection. With
voxel-level precision, our method identified which specific areas are being
paid attention to, identifying these predominant brain regions: the
\emph{hippocampus}, the \emph{amygdala}, the \emph{parahippocampal}, and the
\emph{inferior lateral ventricles}. All these areas are clinically associated
with AD development. Furthermore, our approach consistently found the same
AD-related areas across different cross-validation folds, proving its
robustness and precision in highlighting areas that align closely with known
pathological markers of the disease.
