Abstract
It has been shown that deep neural networks are powerful and flexible models that can be applied on fMRI data with superb representation ability over traditional methods. However, a new challenge of neural network architecture design has also attracted attention: due to the high dimension of fMRI volume images, the manual process of network model design is very time-consuming and error prone. To tackle this problem, we proposed a Particle Swarm Optimization (PSO) based neural architecture search (NAS) framework for a deep belief network (DBN) that models volumetric fMRI data, named NAS-DBN. The core idea is that the particle swarm in our NAS framework can temporally evolve and finally converge to a feasible optimal solution. Experimental results showed that the proposed NAS-DBN framework can find robust architecture with minimal testing loss. Furthermore, we compared functional brain networks derived by NAS-DBN with general linear model (GLM), and the results demonstrated that the NAS-DBN is effective in modeling volumetric fMRI data.
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Acknowledgement
We thank all investigators contributing data to the HCP project. Bao Ge was supported by NSFC61976131.
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Qiang, N., Ge, B., Dong, Q., Ge, F., Liu, T. (2020). Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data. In: Li, Q., Leahy, R., Dong, B., Li, X. (eds) Multiscale Multimodal Medical Imaging. MMMI 2019. Lecture Notes in Computer Science(), vol 11977. Springer, Cham. https://doi.org/10.1007/978-3-030-37969-8_4
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DOI: https://doi.org/10.1007/978-3-030-37969-8_4
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