التفاصيل البيبلوغرافية
| العنوان: |
Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps |
| المؤلفون: |
Träuble, Jakob, Hiscox, Lucy, Johnson, Curtis, Schönlieb, Carola-Bibiane, Schierle, Gabriele Kaminski, Aviles-Rivero, Angelica |
| المصدر: |
Transactions on Machine Learning Research (10/2024) |
| سنة النشر: |
2024 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Computer Science - Machine Learning |
| الوصف: |
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. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2408.00527 |
| رقم الانضمام: |
edsarx.2408.00527 |
| قاعدة البيانات: |
arXiv |