An Interpretable Representation Learning Approach for Diffusion Tensor Imaging
This work addresses interpretability issues in brain connectivity analysis for medical imaging researchers, but it is incremental as it builds on existing representation learning techniques.
The paper tackles the challenge of representing and interpreting Diffusion Tensor Imaging tractography in deep learning by proposing a novel 2D image representation, which improves the F1 score in a sex classification task by 15.74% compared to baseline methods.
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.