Computer Vision News - March 2023

25 with DenseNet Neural Network even more breakthroughs in medical image analysis in the future. Approaches One common approach to using DenseNet for image segmentation is to use a fully convolutional network (FCN). As we talked previously, an FCN takes an image as input and produces a segmentation map, which assigns a class label to each pixel in the image. Now I will show you an example of how a FCN can be implemented using DenseNet in PyTorch: import torch import torch.nn as nn import torch.nn.functional as F from torchvision.models import densenet121 class DensenetFCN(nn.Module): def __init__(self, n_classes): super(DensenetFCN, self).__init__() # Load pretrained Densenet model self.densenet = densenet121(pretrained=True) # Replace final classification layer with 1x1 convolution n_features = self.densenet.classifier.in_features self.densenet.classifier = nn.Conv2d(n_features, n_classes, kernel_size=1) # Upsampling layer self.upsample = nn.Upsample(scale_factor=32, mode='bilinear', align_corners=False) def forward(self, x): features = self.densenet.features(x) out = self.densenet.classifier(features) out = self.upsample(out) return out