使用补丁残留变压器进行阿尔茨海默病诊断的代币高效视觉框架
Fei Huang1, Pengli Zhu2, Nanguang Chen3
1School of Computer Engineering and Science, Shanghai University, Shanghai, China.
Scientific reports
|January 9, 2026
概括
一个新的补丁残留变压器 (PRT) 模型改进了早期阿尔茨海默病 (AD) 诊断和轻度认知障碍 (MCI) 转换预测,使用结构性MRI. 这种人工智能方法增强了对关键大脑区域的关注,优于现有的方法.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 阿尔茨海默病 (AD) 诊断依赖于结构磁共振成像 (sMRI),但当前的AI模型,如视觉变压器 (ViT),面临数据冗余和特征聚焦的挑战.
- 早期发现AD和预测轻度认知障碍 (MCI) 的进展对于有效的干预至关重要.
研究的目的:
- 引入一种新的,代币效率高的框架,即补丁残余变压器 (PRT),用于基于sMRI的增强的AD诊断和MCI转换预测.
- 在ViT模型中解决信息冗余和焦点损失问题,应用于高维神经成像数据.
主要方法:
- PRT模型将sMRI数据分成补丁,并使用一个补丁剩余区块 (PRB),具有Top-K补丁选择和线性拼接补丁令牌融合 (LSPTF).
- 顶级K识别和排名关键补丁,而LSPTF将模型集中在关键区域,提高效率和性能.
- 对阿尔茨海默病神经成像计划 (ADNI) 和成像研究-3 (OASIS-3) 开放访问系列数据集进行了验证.
主要成果:
- 在AD诊断和MCI转化预测方面,PRT模型显著优于现有的切片,补丁,ROI和主体级方法.
- 在不同数据集中,PRT表现出强大的概括能力.
- 产生了一致和强大的突出地图,突出显示了模型的可解释性.
结论:
- PRT框架为利用sMRI数据在AD诊断和MCI转换预测中提供了一种卓越的方法.
- 拟议的PRB,包括Top-K和LSPTF,有效地减轻焦点损失,并提高神经成像分析中的模型性能.
- 在改善阿尔茨海默病的早期检测和干预策略方面,PRT显示出前景.
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