Source-Free Collaborative Domain Adaptation via Multi-Perspective
  Feature Enrichment for Functional MRI Analysis
Yuqi Fang Jinjian Wu Qianqian Wang Shijun Qiu Andrea Bozoki Huaicheng Yan Mingxia Liu
Resting-state functional MRI (rs-fMRI) is increasingly employed in multi-site
research to aid neurological disorder analysis. Existing studies usually suffer
from significant cross-site/domain data heterogeneity caused by site effects
such as differences in scanners/protocols. Many methods have been proposed to
reduce fMRI heterogeneity between source and target domains, heavily relying on
the availability of source data. But acquiring source data is challenging due
to privacy concerns and/or data storage burdens in multi-site studies. To this
end, we design a source-free collaborative domain adaptation (SCDA) framework
for fMRI analysis, where only a pretrained source model and unlabeled target
data are accessible. Specifically, a multi-perspective feature enrichment
method (MFE) is developed for target fMRI analysis, consisting of multiple
collaborative branches to dynamically capture fMRI features of unlabeled target
data from multiple views. Each branch has a data-feeding module, a
spatiotemporal feature encoder, and a class predictor. A mutual-consistency
constraint is designed to encourage pair-wise consistency of latent features of
the same input generated from these branches for robust representation
learning. To facilitate efficient cross-domain knowledge transfer without
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, aiming to obtain a general
feature encoder. Experimental results on three public datasets and one private
dataset demonstrate the efficacy of our method in cross-scanner and cross-study
prediction tasks. The model pretrained on large-scale rs-fMRI data has been
released to the public.