Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task, for example, classifying diseases from healthy controls in fMRI data. We train a Deep Neural Network on Independent components derived from fMRI data using the Independent component analysis (ICA) technique. It learns time direction in the ICA-based data. This pre-trained model is further trained to classify brain disorders in different datasets. Through various experiments, we have shown that learning time direction helps a model learn some causal relation in fMRI data that helps in faster convergence, and consequently, the model generalizes well in downstream classification tasks even with fewer data records.
@article{arxiv.2211.16398,
title = {Self-Supervised Mental Disorder Classifiers via Time Reversal},
author = {Zafar Iqbal and Usman Mahmood and Zening Fu and Sergey Plis},
journal= {arXiv preprint arXiv:2211.16398},
year = {2022}
}