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

Classifying 3D MRI images for early detection of Alzheimer's disease is a
critical task in medical imaging. Traditional approaches using Convolutional
Neural Networks (CNNs) and Transformers face significant challenges in this
domain. CNNs, while effective in capturing local spatial features, struggle
with long-range dependencies and often require extensive computational
resources for high-resolution 3D data. Transformers, on the other hand, excel
in capturing global context but suffer from quadratic complexity in inference
time and require substantial memory, making them less efficient for large-scale
3D MRI data. To address these limitations, we propose the use of Vision Mamba,
an advanced model based on State Space Models (SSMs), for the classification of
3D MRI images to detect Alzheimer's disease. Vision Mamba leverages dynamic
state representations and the selective scan algorithm, allowing it to
efficiently capture and retain important spatial information across 3D volumes.
By dynamically adjusting state transitions based on input features, Vision
Mamba can selectively retain relevant information, leading to more accurate and
computationally efficient processing of 3D MRI data. Our approach combines the
parallelizable nature of convolutional operations during training with the
efficient, recurrent processing of states during inference. This architecture
not only improves computational efficiency but also enhances the model's
ability to handle long-range dependencies within 3D medical images.
Experimental results demonstrate that Vision Mamba outperforms traditional CNN
and Transformer models accuracy, making it a promising tool for the early
detection of Alzheimer's disease using 3D MRI data.

使用 Vision Mamba 模型基于 State Space Models（SSMs）对 3D MRI 图像进行分类以检测阿尔茨海默病，该模型通过动态状态表示和选择性扫描算法，有效地捕获和保留 3D 体积中的重要空间信息，结合卷积运算的并行化特性和状态的高效循环处理，在计算效率和处理长距离依赖性方面表现优于传统的 CNN 和 Transformer 模型。

Vision Mamba: 利用 3D MRI 扫描对阿尔茨海默病进行先进的分类

Vision Mamba: Cutting-Edge Classification of Alzheimer's Disease with 3D  MRI Scans

We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease
classification. Training a 3D convolutional neural network (CNN) is
time-consuming and computationally expensive. We make use of approximate rank
pooling to transform the 3D MRI image volume into a 2D image to use as input to
a 2D CNN. We show our proposed CNN model achieves $9.5\%$ better Alzheimer's
disease classification accuracy than the baseline 3D models. We also show that
our method allows for efficient training, requiring only 20% of the training
time compared to 3D CNN models. The code is available online:
this https URL

本文介绍一种使用 2D CNN 架构对 3D MRI 图像进行阿尔茨海默病分类的方法，使用近似排名池化将 3D MRI 图像转换为 2D 图像进行分类，实验结果表明，相对基线 3D 模型，我们提出的 CNN 模型在阿尔茨海默病分类上获得了更好的准确度，并且训练时间仅需 3D CNN 模型的 20%。