The convolutional layers allow this with the use of a filter. In this case, the filter is a kernel of a specific size (for example 3x3 or 5x5) that moves across the image. For each point on the image, a value is calculated based on the filter using a convolution operation. The advantage of this process is that it is possible to reduce the parameters across the network whereas keeping the information of nearby voxels. After the filters have passed over the image, a feature map is generated for each filter. These are then taken through an activation function, which decides whether a certain feature is present at a given location in the image.