Article

Fed-ComBat: A Generalized Federated Framework for Batch Effect Harmonization in Collaborative Studies

2023-05-24

Abstract excerpt

In neuroimaging research, the utilization of multi-centric analyses is crucial for obtaining sufficient sample sizes and representative clinical populations. Data harmonization techniques are typically part of the pipeline in multi-centric studies to address systematic biases and ensure the comparability of the data. However, most multi-centric studies require centralized data, which may result in exposing individ...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: cabe701b-4973-581a-8fd7-80ea895b961f
DOI: 10.1101/2023.05.24.542107

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Fed-ComBat: A Generalized Federated Framework for Batch Effect Harmonization in Collaborative StudiesDOI 10.1101/2023.05.24.542107Read

A Unified Form of Batch Harmonization Equation for Normative Modeling: A Location–Scale FrameworkSemantic proximity Read

Individualized Per-Site Meta-Federated Feature Learning (iPS-MFFL) for Privacy-Preserving Brain Tumor MRI Classification under non-IID HeterogeneitySemantic proximity Read

Privacy-first health research with federated learningSemantic proximity Read

FedWeight: Mitigating Covariate Shift of Federated Learning on Electronic Health Records Data through Patients Re-weightingSemantic proximity Read

Harmonization techniques for machine learning studies using multi-site functional MRI dataSemantic proximity Read

FedWeight: Mitigating Covariate Shift of Federated Learning on Electronic Health Records Data through Patients Re-weightingSemantic proximity Read

Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular DiseaseSemantic proximity Read

FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing dataSemantic proximity Read

Ten Quick Tips for Biomedical Federated LearningSemantic proximity Read

Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes ProjectSemantic proximity Read

Longitudinal ComBat: A Method for Harmonizing Longitudinal Multi-scanner Imaging DataSemantic proximity Read

A Simple, Privacy-Preserving Multimodal Stacking System for Clinical Progression Prediction in Low-Resource Rare DiseasesSemantic proximity Read