Figure 12.

Convolutional neural networks decompose spatial features into hierarchical structures for classification. A small spatial region (including spectra) is selected as input. The network applies a series of pre-learned convolutional filters to identify a hierarchy of spatial features. The final layer is used for classification, providing a posterior probability for any cell type. Once the spatial features are learned, they can alternatively be used as input to more traditional classifiers, such as support vector machines