ArticleFrontiers in neuroinformatics2022
Multi-Source Domain Adaptation Techniques for Mitigating Batch Effects: A Comparative Study.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Supervised Domain Adaptation Mitigates Cross-Ethnicity Prediction Error in Neuroimaging-Based Cognitive Prediction.bioRxiv : the preprint server for biology · 2026Article
- Single-cell RNA sequencing technology in human spermatogenesis: Progresses and perspectives.Molecular and cellular biochemistry · 2024Review
- Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks.Scientific reports · 2023Article
- On the variability and dependence of human leg stiffness across strides during running and some consequences for the analysis of locomotion data.Royal Society open science · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The past decade has seen an increasing number of applications of deep learning (DL) techniques to biomedical fields, especially in neuroimaging-based analysis. Such DL-based methods are generally data-intensive and require a large number of training instances, which might be infeasible to acquire from a single acquisition site, especially for data, such as fMRI scans, due to the time and costs that they demand. We can attempt to address this issue by combining fMRI data from various sites, thereby creating a bigger heterogeneous dataset. Unfortunately, the inherent differences in the combined data, known as batch effects, often hamper learning a model. To mitigate this issue, techniques such as multi-source domain adaptation [Multi-source Domain Adversarial Networks (MSDA)] aim at learning an effective classification function that uses (learned) domain-invariant latent features. This article analyzes and compares the performance of various popular MSDA methods [MDAN, Domain AggRegation Networks (DARN), Multi-Domain Matching Networks (MDMN), and Moment Matching for MSDA (M
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.