U-net的起源

U-Net于2015年提出，最初是专门为医学图像分割而设计的，经过修改之后，将其用于脑龄预测方面。U-net是一个做分割的卷积神经网络。它将图像作为输入，并将每个像素分类为输出，给出缩放后的图像。

U-net的网络模型图

 U-net的网络模型详细说明（英+中）

U-net is consisted of 18 3x3 convolutional layers with batch normalization and rectified linear units (ReLU), four 2x2 max pooling, four 2x2 up convolutional layers, and one 1x1 convolutional layers. The first half is contracting the image. In each step in the first half, each 7 two 3x3 convolutional layers are followed by one 2x2 max pooling to down sample the image. Each time before the max pooling, the U-net crops part of the output and keeps it for the second half of the U-net. The first half consists of four such steps. In the second half of the U-net, in each step, it first does a 2x2 up convolution and then concatenates the cropped part from the first half to the layer output. This is followed by two 3x3 convolutional layers. The second half consists of four such steps and finally connects to the 1x1 convolutional layer to give the output.

U-net由18个3x3卷积层组成，其中包含批处理归一化和校正线性单元(ReLU)， 4个2x2 max池化，4个2x2 up卷积层和1个1x1卷积层。前半部分是收缩图像。在前半部分的每个步骤中，每7两个3x3卷积层之后是一个2x2 max池来对图像进行下采样。每次在最大池化之前，U-net都会截取部分输出，并将其保留到U-net的后半段。前半部分包括四个这样的步骤。在U-net的后半部分，在每一步中，它首先进行2x2向上卷积，然后将前半部分的裁剪部分连接到层输出。接下来是两个3x3卷积层。后半部分由四个这样的步骤组成，最后连接到1x1卷积层以给出输出。

U-net代码

import torch
import torch.nn as nn
import torch.nn.functional as F

# Double Convolution
class DoubleConv2d(nn.Module):
    def __init__(self, inputChannel, outputChannel):
        super(DoubleConv2d, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(inputChannel, outputChannel, kernel_size=3, padding=1),
            nn.BatchNorm2d(outputChannel),
            nn.ReLU(True),
            nn.Conv2d(outputChannel, outputChannel, kernel_size=3, padding=1),
            nn.BatchNorm2d(outputChannel),
            nn.ReLU(True)
        )

    def forward(self, x):
        out = self.conv(x)
        return out


# Down Sampling
class DownSampling(nn.Module):
    def __init__(self):
        super(DownSampling, self).__init__()
        self.down = nn.MaxPool2d(kernel_size=2)

    def forward(self, x):
        out = self.down(x)
        return out


# Up Sampling
class UpSampling(nn.Module):

    # Use the deconvolution
    def __init__(self, inputChannel, outputChannel):
        super(UpSampling, self).__init__()
        self.up = nn.Sequential(
            nn.ConvTranspose2d(inputChannel, outputChannel, kernel_size=2, stride=2),
            nn.BatchNorm2d(outputChannel)
        )

    def forward(self, x, y):
        x =self.up(x)
        diffY = y.size()[2] - x.size()[2]
        diffX = y.size()[3] - x.size()[3]
        x = F.pad(x, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        out = torch.cat([y, x], dim=1)
        return out


class Unet(nn.Module):
    def __init__(self):
        super(Unet, self).__init__()
        self.layer1 = DoubleConv2d(1, 64)
        self.layer2 = DoubleConv2d(64, 128)
        self.layer3 = DoubleConv2d(128, 256)
        self.layer4 = DoubleConv2d(256, 512)
        self.layer5 = DoubleConv2d(512, 1024)
        self.layer6 = DoubleConv2d(1024, 512)
        self.layer7 = DoubleConv2d(512, 256)
        self.layer8 = DoubleConv2d(256, 128)
        self.layer9 = DoubleConv2d(128, 64)

        self.layer10 = nn.Conv2d(64, 2, kernel_size=3, padding=1)  # The last output layer

        self.down = DownSampling()
        self.up1 = UpSampling(1024, 512)
        self.up2 = UpSampling(512, 256)
        self.up3 = UpSampling(256, 128)
        self.up4 = UpSampling(128, 64)

    def forward(self, x):
        conv1 = self.layer1(x)
        down1 = self.down(conv1)
        conv2 = self.layer2(down1)
        down2 = self.down(conv2)
        conv3 = self.layer3(down2)
        down3 = self.down(conv3)
        conv4 = self.layer4(down3)
        down4 = self.down(conv4)
        conv5 = self.layer5(down4)
        up1 = self.up1(conv5, conv4)
        conv6 = self.layer6(up1)
        up2 = self.up2(conv6, conv3)
        conv7 = self.layer7(up2)
        up3 = self.up3(conv7, conv2)
        conv8 = self.layer8(up3)
        up4 = self.up4(conv8, conv1)
        conv9 = self.layer9(up4)
        out = self.layer10(conv9)
        return out


# Test part

mynet = Unet()
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# mynet.to(device)
input = torch.rand(3, 1, 572, 572)
# output = mynet(input.to(device))
output = mynet(input)
print(output.shape)  # (3,2,572,572)

仅用于个人学习，参考自:

U-Net神经网络_uodgnez的博客-CSDN博客

BRAIN-AGE PREDICTION