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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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