An Interpretable Representation Learning Approach for Diffusion Tensor Imaging
Journal:
arXiv
Published Date:
May 25, 2025
Abstract
Diffusion Tensor Imaging (DTI) tractography offers detailed insights into the
structural connectivity of the brain, but presents challenges in effective
representation and interpretation in deep learning models. In this work, we
propose a novel 2D representation of DTI tractography that encodes tract-level
fractional anisotropy (FA) values into a 9x9 grayscale image. This
representation is processed through a Beta-Total Correlation Variational
Autoencoder with a Spatial Broadcast Decoder to learn a disentangled and
interpretable latent embedding. We evaluate the quality of this embedding using
supervised and unsupervised representation learning strategies, including
auxiliary classification, triplet loss, and SimCLR-based contrastive learning.
Compared to the 1D Group deep neural network (DNN) baselines, our approach
improves the F1 score in a downstream sex classification task by 15.74% and
shows a better disentanglement than the 3D representation.
