Unet Segmentation

U-Net is a neural network architecture used for semantic segmentation, consisting of a contracting path and an expansive path. The contracting path performs downsampling through repeated applications of 3x3 convolutions, ReLU activations, and 2x2 max pooling, with the number of feature channels doubling at each level. The expansive path, on the other hand, performs upsampling via upsampling, 2x2 convolutions, concatenation of feature maps, and 3x3 convolutions. Finally, a 1x1 convolution maps the feature vectors to the desired classes. U-Net has significant application value in medical image analysis and can efficiently and accurately achieve pixel-level classification.