基于多模式域适应的阿尔茨海默病的预测
Binbin Fu1,2, Changsong Shen1,2, Shuzu Liao3
1School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China.
Brain sciences
|June 26, 2025
概括
这项研究引入了一种用于阿尔茨海默病 (AD) 分类的新型多模式深域适应 (MM-DDA) 模型,有效地整合MRI和PET数据以提高诊断准确性.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 阿尔茨海默病 (AD) 诊断受益于结构性MRI和功能性PET扫描.
- 挑战包括不同的数据结构和跨数据集的域移动.
- 现有的方法往往忽略了多模式互补性和领域差异.
研究的目的:
- 为增强AD分类提出一个多模态深域适应 (MM-DDA) 模型.
- 为了利用MRI和PET数据的补充信息.
- 为了减轻数据集之间的域分布差异.
主要方法:
- 开发了一个MM-DDA模型,集成MRI和PET数据.
- 采用CNN来进行特征编码,用于特征融合的多头注意力,以及用于域移动的对抗性学习.
- 利用了来自阿尔茨海默病神经成像计划 (ADNI) 数据集的数据.
主要成果:
- 在转移学习环境中实现了高分类准确性 (例如,ADNI1→ADNI2和ADNI2→ADNI1).
- 对AD与CN的准确度高达94.73%,对于pMCI与sMCI的准确度高达81.81%.
- 在各种诊断比较中表现出强大的性能.
结论:
- 拟议的MM-DDA模型在AD预测任务中显示出卓越的性能.
- 多模式数据和域调整的有效整合大大提高了分类准确性.
- 在转移学习场景中,MM-DDA的表现优于现有的深度学习方法.
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