Abstract
With the ongoing development of neuroimaging technology, neuroimaging classification has become a popular and challenging topic. The high dimension and small sample size characteristics pose many challenges to neuroimaging classification. The traditional neuroimaging classification solutions are tensor-based models, which may not fully consider the structural information and can’t mine the essential features of the input data. Considering the complicated properties of the neuroimaging data, a deep learning based algorithm—the hierarchical convolutional sparse auto-encoder (HCSAE) considering all dimensional information together is proposed in this paper. The HCSAE treats different convolutional sparse auto-encoder (CSAE) in an unsupervised hierarchical mode, where the CSAE extracts the essential features of the input by the sparse auto-encoder (SAE) and encodes the inputs in a convolutional manner, which helps to extract efficient and robust features and conserve abundant detail information for the neuroimaging classification. The proposed algorithm was verified by three human brain fMRI classification datasets, and showed a great potential compared with the traditional classification algorithms.
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Han, X., Zhong, Y., He, L., Yu, P.S., Zhang, L. (2015). The Unsupervised Hierarchical Convolutional Sparse Auto-Encoder for Neuroimaging Data Classification. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_16
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DOI: https://doi.org/10.1007/978-3-319-23344-4_16
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