Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2408.00527

Authors: Träuble, Jakob, Hiscox, Lucy, Johnson, Curtis, Schönlieb, Carola-Bibiane, Schierle, Gabriele Kaminski, Aviles-Rivero, Angelica

Institutions: University of Cambridge, Cardiff University, University of Delaware

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green