MC-ViViT:多分支分类器-ViViT用于检测老年人的轻度认知障碍,使用面部视频
Jian Sun1, Hiroko H Dodge2, Mohammad H Mahoor3
1Department Of Computer Science, University of Denver, 2155 E Wesley Ave, Denver, Colorado, 80210, United States of America.
这项研究引入了一种新的AI模型,MC-ViViT,用于使用视频聊天中的面部特征来检测轻度认知障碍 (MCI). 该模型实现了90.63%的准确性,显示了MCI早期检测的前景.
科学领域:
- 人工智能的人工智能
- 神经科学是一个神经科学.
- 医疗成像医学成像
背景情况:
- 包括CNN在内的深度学习,在使用各种数据类型检测轻度认知障碍 (MCI) 方面表现有前途.
- 来自视频数据的面部特征分析为MCI检测提供了一个新的途径.
研究的目的:
- 提出和评估一种新的多分支分类器-视频视觉转换器 (MC-ViViT) 模型,以区分MCI与正常认知.
- 为应对在MCI检测中不平衡的数据集所带来的挑战.
主要方法:
- 开发MC-ViViT模型,从视频数据中提取时空面部特征.
- 使用多分支分类器 (MC) 模块增强特征表示.
- 引入一种新的损失函数 (HP Loss),结合焦点损失和AD-CORRE损失来处理不平衡的数据.
主要成果:
- MC-ViViT模型在预测MCI方面表现出很高的潜力.
- 在使用I-CONECT数据集中的采访视频预测MCI时获得了90.63%的准确性.
- 拟议的HP损失有效地解决了数据集不平衡问题.
结论:
- 该MC-ViViT模型显示显著的承诺作为一个AI驱动的工具,用于MCI检测.
- 从视频互动中分析面部特征是认知评估的一个可行的方法.
- 开发的HP Loss函数有效地提高了MCI预测不平衡数据集的模型性能.
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