Hello everyone, I am Weixue AI. Today I will introduce to you the application skills of the deep learning actual combat 39-U-Net model in medical image recognition and segmentation, taking the task of cell nucleus segmentation as an example. This article will introduce the method of applying U-Net model in the field of medical image segmentation. We will start from the principle of the U-Net model, use PyTorch to build the model, and show the model code in detail. Next, we will show some sample medical imaging data and load them into the model for training. Finally, we will print the loss and accuracy and test the trained model.
Table of contents
- U-Net model principle
- Use PyTorch to build U-Net model
- Sample Medical Imaging Data
- Data loading and preprocessing
- model training
- model testing
- Summarize
1. U-Net model principle
U-Net is a convolutional neural network (CNN) for image segmentation. U-Net has a wide range of applications in the field of medical image segmentation. Its network structure consists of a shrinking path (encoder) and an expanding path (decoder).
The encoder is responsible for extracting features in the image and consists of multiple convolutional layers, ReLU activation functions, and max pooling layers. The decoder is responsible for mapping the extracted features back to the spatial dimensions of the original image, consisting of multiple convolutional layers, ReLU activation functions, and upsampling layers. In addition, there are skip connections in U-Net to connect features between encoder and decoder to help improve segmentation accuracy.
The mathematical principle of the U-Net model can be expressed by the following formula:
First, the U-Net model consists of an encoder and a decoder. The encoder part is responsible for gradually downsampling the input image into smaller feature maps, while the decoder part gradually downsamples these feature maps