AutoEncoder for Neuroimage.

Variational AutoEncoder (VAE) as a class of neural networks performing nonlinear dimensionality reduction has become an effective tool in neuroimaging analysis. Currently, most studies on VAE consider unsupervised learning to capture the latent representations and to some extent, this strategy may be under-explored in the case of heavy noise and imbalanced neural image dataset. In the reinforcement learning point of view, it is necessary to consider the class-wise capability of decoder. The latent space for autoencoders depends on the distribution of the raw data, the architecture of the model and the dimension of the latent space, combining a supervised linear autoencoder model with variational autoencoder (VAE) may improve the performance of classification. In this paper, we proposed a supervised linear and nonlinear cascade dual autoencoder approach, which increases the latent space discriminative capability by feeding the latent low dimensional space from semi-supervised VAE into a further step of the linear encoder-decoder model. The effectiveness of the proposed approach is demonstrated on brain development. The proposed method also is evaluated on imbalanced neural spiking classification.