Computer Vision News - March 2023

28 Medical Image Segmentation In this example, we load a pre-trained DenseNet model and add a segmentation head to the top of the network. The segmentation head consists of a global average pooling layer, followed by two fully connected layers with dropout and a sigmoid activation function to produce a binary segmentation mask. During training, we can use a binary cross-entropy loss function to compare the output mask to the ground truth segmentation. Another approach to using DenseNet for tumor segmentation is to use a 3D convolutional network. Here is an example of a 3D convolutional network implemented using DenseNet in PyTorch. In the previous code we built a 3D DenseNet. Now let’s create a class which implements a 3D segmentation model: import torch import torch.nn as nn import torch.nn.functional as F from torchvision.models import densenet121 class Densenet3DSegmentation(nn.Module): def __init__(self, n_classes): super(Densenet3DSegmentation, self).__init__()