Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data.

ABSTRACT: Convolutional neural networks (CNNs)-as a type of deep learning-have been specifically designed for highly heterogeneous data, such as natural images. Neuroimaging data, however, is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. To harness spatial homogeneity of neuroimaging data, we suggest here a new CNN architecture that combines the idea of hierarchical abstraction in CNNs with a prior on the spatial homogeneity of neuroimaging data. Whereas early layers are trained globally using standard convolutional layers, we introduce patch individual filters (PIF) for higher, more abstract layers. By learning filters in individual latent space patch

SUBMITTER: Eitel F 

PROVIDER: S-EPMC8712523 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature