Fang Yuqi, Wu Jinjian, Wang Qianqian, Qiu Shijun, Bozoki Andrea, Liu Mingxia
Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Department of Acupuncture and Rehabilitation, The Affiliated Traditional Chinese Medicine Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 510130, China.
Pattern Recognit. 2025 Jan;157. doi: 10.1016/j.patcog.2024.110912. Epub 2024 Aug 22.
Resting-state functional MRI (rs-fMRI) is increasingly employed in multi-site research to analyze neurological disorders, but there exists cross-site/domain data heterogeneity caused by site effects such as differences in scanners/protocols. Existing domain adaptation methods that reduce fMRI heterogeneity generally require accessing source domain data, which is challenging due to privacy concerns and/or data storage burdens. To this end, we propose a source-free collaborative domain adaptation (SCDA) framework using only a pretrained source model and unlabeled target data. Specifically, a multi-perspective feature enrichment method (MFE) is developed to dynamically exploit target fMRIs from multiple views. To facilitate efficient source-to-target knowledge transfer without accessing source data, we initialize MFE using parameters of a pretrained source model. We also introduce an unsupervised pretraining strategy using 3,806 unlabeled fMRIs from three large-scale auxiliary databases. Experimental results on three public and one private datasets show the efficacy of our method in cross-scanner and cross-study prediction.
静息态功能磁共振成像（rs-fMRI）越来越多地用于多中心研究以分析神经疾病，但由于扫描仪/协议差异等场所效应，存在跨场所/领域的数据异质性。现有的减少功能磁共振成像异质性的领域适应方法通常需要访问源域数据，由于隐私问题和/或数据存储负担，这具有挑战性。为此，我们提出了一种仅使用预训练源模型和未标记目标数据的无源协作域适应（SCDA）框架。具体而言，开发了一种多视角特征增强方法（MFE）以从多个视图动态利用目标功能磁共振成像。为了在不访问源数据的情况下促进从源到目标的高效知识转移，我们使用预训练源模型的参数初始化MFE。我们还引入了一种无监督预训练策略，使用来自三个大规模辅助数据库的3806个未标记功能磁共振成像。在三个公共数据集和一个私有数据集上的实验结果表明了我们的方法在跨扫描仪和跨研究预测中的有效性。
