Abstract #3858

Automatic Identification of ICA Components using A Generative Adversarial Network

Yiyu Chou¹, Snehashis Roy¹, Catie Chang², John Butman³, and Dzung L Pham¹

¹Center for Neuroscience and Regenerative Medicine, Bethesda, MD, United States, ²Laboratory of Functional and Molecular Imaging, NINDS, Bethesda, MD, United States, ³Radiology and Imaging Sciences, NIH, Bethesda, MD, United States

Manual classification of the components derived from ICA analysis of rsfMRI data as particular functional brain resting state networks (RSNs) can be labor intensive and requires expertise; hence, a fully automatic algorithm that can reliably classify these RSNs is desirable. In this paper, we introduce a generative adversarial network (GAN) based method for performing this task. The proposed method achieves over 93% classification accuracy and out-performs the traditional convolutional neural network (CNN) and template matching methods.

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