Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis.
Pattern Recognit
; 1572025 Jan.
Article
in En
| MEDLINE
| ID: mdl-39246820
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Pattern Recognit
/
Pattern recognit
/
Pattern recognition
Year:
2025
Document type:
Article
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