Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors.

ABSTRACT:

Background

In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability.

Purpose

To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction.

Study type

Retrospective.

Population

Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects

SUBMITTER: Bashyam VM 

PROVIDER: S-EPMC8844038 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature