Sparsely Labeled fMRI Data Denoising with Meta-learning-Based Semi-supervised Domain Adaptation

Keun-Soo Heo, Ji-Wung Han, Soyeon Bak, Minjoo Lim, Bogyeong Kang, Sang-Jun Park, Weili Lin, Han Zhang, Dinggang Shen, Tae-Eui Kam

Abstract: Functional magnetic resonance imaging (fMRI) denoising is a crucial preprocessing step in neuroimaging studies, as noise degrades the reliability of downstream analyses. Previous approaches for fMRI denoising either rely on predefined noise patterns or train dataset-specific models, restricting their reliability across various datasets due to inter-dataset variations in scanner types, scanning protocols, and preprocessing pipelines. Additionally, applying previous approaches to new datasets requires extensive expert signal/noise annotations. To mitigate this reliance, leveraging existing datasets to train sparsely labeled datasets is a practical solution, but inconsistencies in labeling criteria hinder effective adaptation. To address these challenges, we propose a meta-learning-based semi-supervised domain adaptation framework, enabling the learning of dataset-irrelevant features from sparsely labeled datasets by leveraging existing labeled datasets with two key components: (1) a dataset-irrelevant feature extractor trained by meta-learning to capture noise patterns across multiple datasets, and (2) dataset-specific classifiers optimized by decoupled training to handle inconsistencies in labeling criteria. Our proposed approach shows outstanding performance on four fMRI datasets in both fully labeled and sparsely labeled conditions.

External IDs:dblp:conf/miccai/HeoHBLKPLZSK25