Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps
This work addresses the problem of generalizing across non-uniformly distributed medical imaging data for researchers in neuroimaging, offering an incremental improvement with a new loss function and the first use of stiffness maps in self-supervised learning.
The paper tackled brain age prediction from 3D stiffness maps by introducing a novel contrastive loss that adapts to localized neighborhoods, achieving superior performance over state-of-the-art methods.
In the field of neuroimaging, accurate brain age prediction is pivotal for uncovering the complexities of brain aging and pinpointing early indicators of neurodegenerative conditions. Recent advancements in self-supervised learning, particularly in contrastive learning, have demonstrated greater robustness when dealing with complex datasets. However, current approaches often fall short in generalizing across non-uniformly distributed data, prevalent in medical imaging scenarios. To bridge this gap, we introduce a novel contrastive loss that adapts dynamically during the training process, focusing on the localized neighborhoods of samples. Moreover, we expand beyond traditional structural features by incorporating brain stiffness - a mechanical property previously underexplored yet promising due to its sensitivity to age-related changes. This work presents the first application of self-supervised learning to brain mechanical properties, using compiled stiffness maps from various clinical studies to predict brain age. Our approach, featuring dynamic localized loss, consistently outperforms existing state-of-the-art methods, demonstrating superior performance and paving the way for new directions in brain aging research.