Generalization of shift invariant neural networks: Image processing of corneal endothelium

In training neural networks, if a training set is not randomly extracted from the whole input domain, it is difficult to construct well generalized neural networks. In this paper, to solve the problem in image processing of corneal endothelium, we propose a method to get a well generalized shift-invariant neural network by designing optimal weights between the input and first hidden layers. The neural network has weight patterns between each layer, and its signal propagation is calculated by discrete convolution between the weight patterns and data in lower layers. The optimal weights between the input and first hidden layer are designed by observing the training set distribution in the vector space. The designed weights transform the training set to cover the whole input domain. In the learning process, the designed weights are fixed and the other weights are updated. In the experiment, well generalized neural networks are obtained.