Convolutional neural networks
Jonas Teuwen, Nikita Moriakov, in Handbook of Medical Image Computing and Computer Assisted Intervention, 2020
20.1 Introduction
Convolutional neural networks (CNNs) – or convnets, for short – have in recent years achieved results which were previously considered to be purely within the human realm. In this chapter we introduce CNNs, and for this we first consider regular neural networks, and how these methods are trained. After introducing the convolution, we introduce CNNs. They are very similar to the regular neural networks as they are also made up of neurons with learnable weights. But, in contrast to MLPs, CNNs make the explicit assumption that inputs have specific structure like images. This allows encoding this property into the architecture by sharing the weights for each location in the image and having neurons respond only locally.