sMRI-PatchNet：一种新颖的可解释的基于块深度学习方法，用于阿尔茨海默病诊断和结构MRI的判别性萎缩定位
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI
- Manchester Metropolitan University（曼彻斯特城市大学）
- Brunel University（布鲁内尔大学）
- University of Sheffield（谢菲尔德大学）
- Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出sMRI-PatchNet，利用SHAP可解释块选择和带位置嵌入的块网络，实现阿尔茨海默病诊断和判别性萎缩定位，并预测MCI转换。
AI中文摘要：
结构磁共振成像（sMRI）凭借其对软组织的高对比度和高空间分辨率，能够识别细微的脑部变化，已被广泛用于诊断神经系统脑疾病，如阿尔茨海默病（AD）。然而，3D高分辨率数据的规模对数据分析和处理提出了重大挑战。由于只有少数脑区表现出与AD高度相关的结构变化，将整个图像数据划分为若干小的规则块的基于块的方法，在更高效的基于sMRI的图像分析中显示出前景。基于块的方法在sMRI上的主要挑战包括识别判别性块、组合来自离散判别性块的特征，以及设计合适的分类器。本工作提出了一种新颖的基于块的深度学习网络（sMRI-PatchNet），具有可解释的块定位和选择功能，用于使用sMRI进行AD诊断。具体而言，它包含两个主要组成部分：1）一种快速高效的可解释块选择机制，通过计算SHapley Additive exPlanations（SHAP）对用于大规模医学数据上AD诊断的迁移学习模型的贡献来确定最具判别性的块；2）一种新颖的基于块的网络，用于从选定的块中提取深度特征并进行AD分类，通过位置嵌入保留位置信息，能够捕获块间和块内的全局和局部信息。该方法已应用于AD分类以及真实数据集上中度认知障碍（MCI）转换的过渡状态预测。
英文摘要：
Structural magnetic resonance imaging (sMRI) can identify subtle brain changes due to its high contrast for soft tissues and high spatial resolution. It has been widely used in diagnosing neurological brain diseases, such as Alzheimer disease (AD). However, the size of 3D high-resolution data poses a significant challenge for data analysis and processing. Since only a few areas of the brain show structural changes highly associated with AD, the patch-based methods dividing the whole image data into several small regular patches have shown promising for more efficient sMRI-based image analysis. The major challenges of the patch-based methods on sMRI include identifying the discriminative patches, combining features from the discrete discriminative patches, and designing appropriate classifiers. This work proposes a novel patch-based deep learning network (sMRI-PatchNet) with explainable patch localisation and selection for AD diagnosis using sMRI. Specifically, it consists of two primary components: 1) A fast and efficient explainable patch selection mechanism for determining the most discriminative patches based on computing the SHapley Additive exPlanations (SHAP) contribution to a transfer learning model for AD diagnosis on massive medical data; and 2) A novel patch-based network for extracting deep features and AD classfication from the selected patches with position embeddings to retain position information, capable of capturing the global and local information of inter- and intra-patches. This method has been applied for the AD classification and the prediction of the transitional state moderate cognitive impairment (MCI) conversion with real datasets.
