DML-MFCM:一个基于深度度度度学习的多式微粒度分类模型,用于阿尔茨海默病诊断
Heng Wang1, Tiejun Yang2,3,4, Jiacheng Fan1
1School of Information Science and Engineering, Henan University of Technology, Zhengzhou, China.
Journal of X-ray science and technology
|February 20, 2025
概括
这项研究引入了一种新的深度度度度学习模型,用于早期阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 诊断. 该模型使用多式联络数据准确区分AD和MCI的不同阶段.
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) 诊断和早期干预对于延迟神经退行至关重要.
- 结构磁共振成像 (sMRI) 在轻度认知障碍 (MCI) 中显示微妙的大脑变化,这给诊断带来了挑战.
- 现有的多式联络诊断模型往往忽视了多式联络关系.
研究的目的:
- 提出一个多式细粒度分类模型用于AD诊断 (DML-MFCM).
- 利用深度度度度学习来从sMRI中提取增强的特征.
- 捕捉多式联运数据中的潜在关系,以提高诊断准确度.
主要方法:
- 一个细粒度的特征提取模块来识别微妙的损伤区域特征.
- 一个多式联络交叉注意力机制,用于整合和学习不同类型的数据.
- 一个混合深度度度指标学习损失函数,以优化样本表示和分类.
主要成果:
- 在ADNI数据集上取得的高精度:98.75% (AD与NC),95.88% (MCI与NC) 和88.00% (sMCI与pMCI).
- 在AIBL数据集上表现强:94.33% (AD与NC) 和91.67% (MCI与NC).
- 拟议的DML-MFCM显示出卓越的诊断能力.
结论:
- 开发的DML-MFCM在诊断阿尔茨海默病方面表现出色.
- 该模型有效地利用多模式信息来准确检测AD和MCI.
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