Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome

Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome

Very preterm (VPT) infants (born at less than 32 weeks gestational age) are at high risk for various adverse neurodevelopmental deficits. Unfortunately, most of these deficits cannot be accurately diagnosed until the age of 2-5 years old. Given the benefits of early interventions, accurate diagnosis...

Full description

Bibliographic Details
Main Authors: Hailong Li, Junqi Wang, Zhiyuan Li, Kim M. Cecil, Mekibib Altaye, Jonathan R. Dillman, Nehal A. Parikh, Lili He
Format: Article
Language:English
Published: Elsevier 2024-05-01
Series:NeuroImage
Subjects:
Deep learning
Graph convolutional network
Supervised contrastive learning
Diffusion tensor imaging
Brain structural connectome
Early prediction
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811924000740

Similar Items

Similar Items