Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from
  MRIs
Zhaonian Zhang Richard Jiang
The integration of machine learning in medicine has significantly improved
diagnostic precision, particularly in the interpretation of complex structures
like the human brain. Diagnosing challenging conditions such as Alzheimer's
disease has prompted the development of brain age estimation techniques. These
methods often leverage three-dimensional Magnetic Resonance Imaging (MRI)
scans, with recent studies emphasizing the efficacy of 3D convolutional neural
networks (CNNs) like 3D ResNet. However, the untapped potential of Vision
Transformers (ViTs), known for their accuracy and interpretability, persists in
this domain due to limitations in their 3D versions. This paper introduces
Triamese-ViT, an innovative adaptation of the ViT model for brain age
estimation. Our model uniquely combines ViTs from three different orientations
to capture 3D information, significantly enhancing accuracy and
interpretability. Tested on a dataset of 1351 MRI scans, Triamese-ViT achieves
a Mean Absolute Error (MAE) of 3.84, a 0.9 Spearman correlation coefficient
with chronological age, and a -0.29 Spearman correlation coefficient between
the brain age gap (BAG) and chronological age, significantly better than
previous methods for brian age estimation. A key innovation of Triamese-ViT is
its capacity to generate a comprehensive 3D-like attention map, synthesized
from 2D attention maps of each orientation-specific ViT. This feature is
particularly beneficial for in-depth brain age analysis and disease diagnosis,
offering deeper insights into brain health and the mechanisms of age-related
neural changes.