通过功能性MRI分析的多视角功能丰富来实现无源协作域调整
Yuqi Fang1, Jinjian Wu2, Qianqian Wang1
1Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Pattern recognition
|September 9, 2024
概括
这项研究引入了一种新的无源域适应框架,用于静止状态功能性MRI (rs-fMRI) 数据. 该方法有效地减少了跨站点的数据异质性,而不需要原始源数据,改进了神经疾病分析.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 休息状态功能性MRI (rs-fMRI) 对于多部位神经疾病研究至关重要.
- 由于扫描仪和协议的变化,跨站点数据异质性构成了重大挑战.
- 当前的域名适应技术往往需要访问源域名数据,引发隐私和存储问题.
研究的目的:
- 提出一个新的无源代码协作域调整 (SCDA) 框架.
- 通过消除对源数据访问的需求,解决现有方法的局限性.
- 通过使用异构的rs-fMRI数据来增强神经系统疾病的分析.
主要方法:
- 开发了一个无源代码的协作域调整 (SCDA) 框架.
- 引入了多视角特征丰富 (MFE) 方法,以从多个视图中利用目标fMRI数据.
- 在大型数据集 (3806个未标记的fMRIs) 上采用无监督预训练策略,并使用预训练的源模型初始化MFE以实现高效的知识传输.
主要成果:
- 证明了SCDA框架在减少rs-fMRI数据异质性的有效性.
- 取得了成功的交叉扫描器和交叉研究预测性能.
- 在三个公共和一个私人数据集上验证了该方法.
结论:
- 拟议的SCDA框架为多站点rs-fMRI研究中的域调整提供了可行的解决方案.
- 它有效地减轻了跨站点数据异质性,而不需要访问源数据.
- 这种方法有助于对神经系统疾病进行更强大,更普遍的分析.
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