Self-Supervised Mental Disorder Classifiers via Time Reversal

Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques are required to combat this problem. In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in the downstream classification tasks, for example, classifying diseases from healthy controls in fMRI data. We train a Deep Neural Network on Independent components derived from fMRI data using Independent component analysis (ICA) to learn time direction. This pre-trained model is further trained to classify brain disorders in different datasets. Through various experiments, we show that learning time direction helps a model learn some causal relation in fMRI data that helps in faster convergence and better generalization across various datasets, even with fewer data records available for training.