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.

这项研究提出了一种创新的方法来诊断阿尔茨海默病，使用设计用于增强模型决策可解释性的 3D MRI。我们的方法采用了软注意力机制，使 2D CNN 能够提取体积表示。同时，学习每个切片在决策中的重要性，生成了一个能够解释的 MRI 的体素级注意力图。通过使用阿尔茨海默病神经影像学计划（ADNI）的一个标准化的 MRI 数据集来测试我们的方法并确保结果的可重复性。在这个数据集上，我们的方法在两个任务中显著优于最先进的方法：（i）将 AD 与正常认知（CN）区分，准确率为 0.856，Matthew's 相关系数（MCC）为 0.712，分别比第二名提高了 2.4％和 5.3％；（ii）在区分稳定和进展性轻度认知障碍的预测任务中，准确率为 0.725，MCC 为 0.443，比第二名分别提高了 10.2％和 20.5％。我们通过采用双重迁移学习策略实现了这一预测结果，增强了对形态变化的敏感性，并促进了早期 AD 的检测。通过体素级精确度，我们的方法确定了哪些特定区域受到关注，并确定了这些主导脑区域：海马、杏仁核、视角回、下侧脑室。所有这些区域与 AD 的发展有关。此外，我们的方法在不同的交叉验证折叠中始终找到了相同的与 AD 相关的区域，证明了其在突出与该疾病的已知病理标志密切相关的区域方面的稳健性和精确性。

AXIAL：基于 2D CNN 的 3D MRI 脑部扫描的阿尔茨海默病定位诊断可解释性的注意力机制

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

Information from neuroimaging examinations (CT, MRI) is increasingly used to
support diagnoses of dementia, e.g., Alzheimer's disease. While current
clinical practice is mainly based on visual inspection and feature engineering,
Deep Learning approaches can be used to automate the analysis and to discover
new image-biomarkers. Part-prototype neural networks (PP-NN) are an alternative
to standard blackbox models, and have shown promising results in general
computer vision. PP-NN's base their reasoning on prototypical image regions
that are learned fully unsupervised, and combined with a simple-to-understand
decision layer. We present PIPNet3D, a PP-NN for volumetric images. We apply
PIPNet3D to the clinical case study of Alzheimer's Disease diagnosis from
structural Magnetic Resonance Imaging (sMRI). We assess the quality of
prototypes under a systematic evaluation framework, propose new metrics to
evaluate brain prototypes and perform an evaluation with domain experts. Our
results show that PIPNet3D is an interpretable, compact model for Alzheimer's
diagnosis with its reasoning well aligned to medical domain knowledge. Notably,
PIPNet3D achieves the same accuracy as its blackbox counterpart; and removing
the remaining clinically irrelevant prototypes from its decision process does
not decrease predictive performance.

使用神经影像学（CT，MRI）数据以支持痴呆症的诊断，特别是阿尔茨海默病。通过使用深度学习方法自动分析并发现新的图像生物标记，我们提出了一种基于部分原型神经网络（PP-NN）的模型 PIPNet3D，并应用于阿尔茨海默病的临床研究。结果表明，PIPNet3D 是一种可解释的、紧凑的阿尔茨海默病诊断模型，其推理过程与医学领域知识相吻合，且具有与其黑盒模型相同的准确度。