Self-Supervised Mental Disorder Classifiers via Time Reversal

• URL: http://arxiv.org/abs/2211.16398v2
• Date: Wed, 30 Nov 2022 18:18:10 GMT
• Title: Self-Supervised Mental Disorder Classifiers via Time Reversal
• Authors: Zafar Iqbal, Usman Mahmood, Zening Fu, Sergey Plis
• Abstract summary: We demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task. We train a Deep Neural Network on Independent components derived from fMRI data using the Independent component analysis (ICA) technique. We show that learning time direction helps a model learn some causal relation in fMRI data that helps in faster convergence.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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