ViViMZheimer是一个基于切片的端到端模型,用于从3DMRI中对阿尔茨海默病的诊断
Juan Zhou1, Jiahui Wan1, Xia Chen2
1School of Information and Software Engineering, East China Jiaotong University, 330013, Nanchang, China.
Scientific reports
|December 7, 2025
概括
ViViMZheimer提供了一个端到端的解决方案,用于使用3DMRI扫描进行阿尔茨海默病 (AD) 诊断. 这种计算效率高的模型可以准确地识别AD,轻度认知障碍 (MCI) 和认知正常的个体.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 神经科学和神经病学 神经科学和神经病学
背景情况:
- 基于切片的模型在阿尔茨海默病 (AD) 识别中很常见,但缺乏端到端处理,并且可能是计算密集的.
- 现有的方法通常需要手动切片提取,并使用变压器进行高效的切片间依赖模型.
研究的目的:
- 从3DMRI数据直接开发一个端到端,计算效率高的模型,用于自动化阿尔茨海默病诊断.
- 在处理管道和计算复杂性方面克服传统切片式模型的局限性.
主要方法:
- 提出了ViViMZheimer,这是一个端到端的基于切片的模型,集成了ViViT启发的空间编码器和基于Mamba的时间机制.
- 实现了一个轻量级的空间注意力模块和一个封闭的瓶卷积,以提高功能精细化.
- 保持线性计算复杂性,以在三个空间方向上高效地进行切割间依赖模型.
主要成果:
- 在区分阿尔茨海默病 (AD) 和认知正常 (CN) 个体 (98.17%) 中取得了高精度.
- 在ADNI数据集上,在区分AD与轻度认知障碍 (MCI) (82.21%) 和CN与MCI (83.15%) 中表现出强的表现.
- ViViMZheimer证明了自动诊断的有效性和计算效率.
结论:
- ViViMZheimer提供了一种新的,端到端的方法,用于使用3DMRI诊断阿尔茨海默病.
- 该模型的效率和准确性凸显了其在神经退行性疾病识别中临床应用的潜力.
- 这种方法解决了以前基于切片的方法的关键局限性,提供了一个更简单的诊断工具.
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