Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps

• URL: http://arxiv.org/abs/2408.00527v2
• Date: Sun, 10 Nov 2024 21:37:07 GMT
• Title: Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps
• Authors: Jakob Träuble, Lucy Hiscox, Curtis Johnson, Carola-Bibiane Schönlieb, Gabriele Kaminski Schierle, Angelica Aviles-Rivero,
• Abstract summary: We introduce a novel contrastive loss that adapts dynamically during the training process, focusing on the localized neighborhoods of samples. This work presents the first application of self-supervised learning to brain mechanical properties, using compiled stiffness maps to predict brain age.
• Score: 8.14243193774551
• License:
• Abstract: 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.

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