变压器和CNN学习不同的大脑年龄的概念吗?
Nys Tjade Siegel1, Dagmar Kainmueller2,3,4, Fatma Deniz5,6
1Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany.
Human brain mapping
|June 9, 2025
概括
变压器和卷积神经网络在从MRI扫描中预测大脑年龄方面表现相似. 不同的深度学习模型捕捉到可比的大脑衰老效应,确保可靠的生物标志物分析.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 生物标志物开发 生物标志物开发
背景情况:
- 预测的大脑年龄是使用MRI机器学习来预测大脑健康的生物标志物.
- 卷积神经网络 (CNN) 是目前用于大脑年龄预测的最新技术.
- 变压器代表了一个新的深度学习架构,具有提高性能的潜力.
研究的目的:
- 为了比较变压器模型与CNN的性能,以预测大脑年龄.
- 调查不同的深度学习架构是否捕捉到大脑衰老的不同方面.
- 从各种模型中分析大脑年龄预测的实际含义和临床实用性.
主要方法:
- 适应的简单视觉变压器 (sViT) 和转移窗口变压器 (SwinT) 模型.
- 与使用ResNet50 (CNN) 架构的变压器模型进行比较.
- 利用来自英国生物银行数据集的46,381张T1加权结构MR图像.
主要成果:
- 在SwinT和ResNet50中,大脑年龄预测的准确性相似.
- 变压器模型 (sViT,SwinT) 和ResNet50捕获了类似的大脑衰老模式.
- 在跨架构的大脑年龄预测结构中没有观察到实质性的差异.
结论:
- 深度学习模型架构似乎不会混大脑年龄研究.
- 变压器在预测大脑年龄方面表现有前途,并有可能进一步改进.
- 模型架构的选择不会显著改变对大脑年龄生物标志物的解释.
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