What type of study is this?

This is a Quantitative Study study (also classified as: Experimental Study).

September 17, 2025

Imputing Longitudinal Infant Brain MRI Features Using a Self-Supervised Transformer Model

CNC. C. NingTibet University RZRuoke ZhaoZhejiang University YCYiwei ChenNational Taiwan University

Key Points

The model achieves a 55.7% to 55.8% reduction in mean absolute error for various cortical features.
It enhances longitudinal predictions by 19.1% in mean squared error compared to traditional benchmarks.
An innovative self-supervised approach and time-level imputation loss were utilized for enhanced performance.
This method could significantly enrich longitudinal analyses of infant brain development and disease.

Abstract

Motivation: Morphological changes of infant brain provide critical information of brain development and disease progress. However, inevitable data loss of follow-up visit hinders the longitudinal study of neural development. Goal(s): To develop a robust deep learning method which can impute missing MRI in longitudinal studies. Approach: A transformer-based model was proposed with self-supervised learning framework and a time-level imputation loss. Results: The proposed model demonstrated superior performance with reduced MAE by 55.7%, 55.8%, 28.1%, 12.9%, and 14.6% for four cortical features compared to the benchmarks of BRITS, USGAN, TIMESNET, SAITS, and original Transformer. It also enhanced downstream longitudinal prediction task by 19.1% in MSE. Impact: The proposed model is able to impute the missing data in longitudinal studies of infants, which may enrich the information along development trajectory and downstream analyses.

AI에게 질문

Bookmark

View Full Paper