Self-Supervised Mental Disorder Classifiers via Time Reversal
This work addresses data scarcity for medical researchers by enabling more efficient classification of brain disorders using fMRI data, though it is incremental as it builds on existing pre-training and ICA techniques.
The paper tackled the problem of data scarcity in medical domains by proposing a self-supervised pre-training method that learns the time direction in fMRI data to improve classification of brain disorders, resulting in faster convergence and better generalization with fewer data records.
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.