Contrastive Learning with Dynamic Localized Repulsion for Brain Age Prediction on 3D Stiffness Maps
<|reference_start|>Contrastive Learning with Dynamic Localized Repulsion for Brain Age Prediction on 3D Stiffness Maps: 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 laying the way for new directions in brain aging research.<|reference_end|>
@article{träuble2024contrastive,
    title={Contrastive Learning with Adaptive Neighborhoods for Brain Age
  Prediction on 3D Stiffness Maps},
    author={Jakob Tr"auble, Lucy Hiscox, Curtis Johnson, Carola-Bibiane
  Sch"onlieb, Gabriele Kaminski Schierle, Angelica Aviles-Rivero},
    journal={Transactions on Machine Learning Research (10/2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00527},
    primaryClass={cs.LG}
}