阿尔茨海默病类别数据集拆分
提供阿尔茨海默病类别数据集的训练、验证和测试拆分，包含MRI图像和RNA文本数据，用于高精度分类。
基本信息
资源简介
阿尔茨海默病类别数据集拆分
【数据集背景】
本数据集源站标题为《Dataset split for Alzheimer’s disease classes.》。配套论文《Leveraging transformers and explainable AI for Alzheimer’s disease interpretability》发表于 PLOS One（2025），DOI：10.1371/journal.pone.0322607。源站首次发布：2025-05-23。
【数据内容】
源站原始描述：Alzheimer’s disease (AD) is a progressive brain ailment that causes memory loss, cognitive decline, and behavioral changes. It is quite concerning that one in nine adults over the age of 65 have AD. Currently there is almost no cure for AD except very few experimental treatments. However, early detection offers chances to take part in clinical trials or other investigations looking at potential new and effective Alzheimer’s treatments. To detect Alzheimer’s disease, brain scans such as computed…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 3.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0322607.t003。
【主题与分类】
源站学科分类：Cell Biology、Neuroscience、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mental Health。主题标签：use computer vision、local interpretable model、computer vision algorithms、causes memory loss、alzheimer &# 8217、80 &# 8211、progressive brain ailment、utilize advanced transformers、transformers &# 8217、positron emission tomography。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Dataset_split_for_Alzheimer_s_disease_classes_/29137845
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Anzum, Humaira; Sammo, Nabil Sadd; Akhter, Shamim (2025). Dataset split for Alzheimer’s disease classes.. PLOS ONE. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Dataset split for Alzheimer’s disease classes.
- DOI：10.1371/journal.pone.0322607.t003
- 发布日期：2025-05-23
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Dataset_split_for_Alzheimer_s_disease_classes_/29137845
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/54783474"
文件清单
Table 3.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Alzheimer’s disease (AD) is a progressive brain ailment that causes memory loss, cognitive decline, and behavioral changes. It is quite concerning that one in nine adults over the age of 65 have AD. Currently there is almost no cure for AD except very few experimental treatments. However, early detection offers chances to take part in clinical trials or other investigations looking at potential new and effective Alzheimer’s treatments. To detect Alzheimer’s disease, brain scans such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) can be performed. Many researches have been undertaken to use computer vision on MRI images, and their accuracy ranges from 80–90%, new computer vision algorithms and cutting-edge transformers have the potential to improve this performance.We utilize advanced transformers and computer vision algorithms to enhance diagnostic accuracy, achieving an impressive 99% accuracy in categorizing Alzheimer’s disease stages through translating RNA text data and brain MRI images in near-real-time. We integrate the Local Interpretable Model-agnostic Explanations (LIME) explainable AI (XAI) technique to ensure the transformers’ acceptance, reliability, and human interpretability. LIME helps identify crucial features in RNA sequences or specific areas in MRI images essential for diagnosing AD.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0322607.t003（source: https://plos.figshare.com/articles/dataset/Dataset_split_for_Alzheimer_s_disease_classes_/29137845）。
需要用于商业化训练或医疗器械注册（NMPA/FDA）？
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