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 produce 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 hippocampus, the amygdala, the parahippocampal, and the 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.

翻译：本研究提出了一种创新的阿尔茨海默病诊断方法，该方法利用3D MRI数据设计，旨在增强模型决策的可解释性。我们的方法采用软注意力机制，使2D CNN能够提取体积表征。同时，模型学习决策过程中每个切片的重要性，从而生成体素级注意力图以产生可解释的MRI。为验证方法并确保结果可复现，我们选用阿尔茨海默病神经影像倡议（ADNI）的标准MRI数据集。在该数据集上，我们的方法在以下两方面显著优于现有最佳方法：（i）区分阿尔茨海默病与认知正常（CN）的准确率达0.856，马修斯相关系数（MCC）为0.712，较次优方法分别提升2.4%和5.3%；（ii）在区分稳定型与进展型轻度认知障碍（MCI）的预后任务中，准确率达0.725，MCC为0.443，较次优方法分别提升10.2%和20.5%。我们通过采用双重迁移学习策略实现该预后结果，该策略增强了对形态学变化的敏感性，并促进了早期阿尔茨海默病检测。借助体素级精度，我们的方法能识别被关注的具体脑区，主要包括：海马体、杏仁核、海马旁回及下侧脑室。这些区域在临床上均与阿尔茨海默病发展相关。此外，在不同交叉验证折次中，我们的方法始终能发现相同的阿尔茨海默病相关脑区，证明了其在突出与已知疾病病理标志物高度吻合区域方面的鲁棒性和精确性。