Poster
in
Workshop: UniReps: Unifying Representations in Neural Models
Auxiliary objectives improve generalization performance but reduce model specification for low-data neuroimaging-based brain age prediction
Donghyun Kim · Eloy Geenjaar · Vince Calhoun
Keywords: [ Structural magnetic resonance imaging ] [ Generalization ] [ Neural network ] [ Age ] [ Underspecification ]
Data scarcity challenges in healthcare applications impede the ability of machine learning models to generalize effectively. In this work, we propose to add an auxiliary objective to a brain age prediction model that significantly improves model performance and generalization under low-data regimes. We evaluate the impact of the auxiliary objective on model specification and particularly quantify how much random variations in the training process affect a model's representations and predictions. Our results show that while auxiliary objectives enhance generalization and performance, especially in data-limited settings, they also reduce model specification. These findings underscore the trade-off between improving generalization with added constraints such as auxiliary losses, and their reduction in model specification in low-data neuroimaging applications.