[2J4] [General Session] 13. AI Application

[2J4-03] Mental Disorder Diagnosis using fMRI Images by Structured Deep Generative Model

〇Tetsuo Tashiro¹, Takashi Matsubara¹, Kuniaki Uehara¹ (1. Kobe University)

Keywords:Deep learning, fMRI, Deep generative model, Mental Disorder

In mental disorder diagnosis based on fMRI images, conventional studies perform preprocessing such as feature-extraction using correlation analysis since the fMRI dataset is composed of a small number of high-dimensional samples. However, this preprocessing could miss features necessary for diagnosis. On the other hand, deep generative models achieved good accuracy even with a small dataset with limited preprocessing. In this paper, we model fMRI brain images using a deep generative model with a subject-wise variable. The proposed model explicitly separates individual differences from the mental disorder and noise in fMRI images. The proposed model achieves accuracy higher than conventional methods.