利用unet模型来实现新冠肺炎CT和普通肺炎CT扫描分割pytorch代码实现

以下是使用 PyTorch 实现 U-Net 模型进行肺部 CT 扫描分割的代码。首先，我们需要导入所需的库：

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

from torch.utils.data import DataLoader

import torchvision.transforms as transforms

import numpy as np
import matplotlib.pyplot as plt

from tqdm import tqdm

from sklearn.metrics import jaccard_score

import os
import random
from PIL import Image

接下来，我们定义 U-Net 模型：

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

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


class UNet(nn.Module):
    def __init__(self, in_channels=1, out_channels=1, features=[64, 128, 256, 512]):
        super().__init__()
        self.ups = nn.ModuleList()
        self.downs = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        # Down part of UNet
        for feature in features:
            self.downs.append(DoubleConv(in_channels, feature))
            in_channels = feature

        # Up part of UNet
        for feature in reversed(features):
            self.ups.append(
                nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2)
            )
            self.ups.append(DoubleConv(feature*2, feature))

        self.bottleneck = DoubleConv(features[-1], features[-1]*2)
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)

    def forward(self, x):
        skip_connections = []

        for down in self.downs:
            x = down(x)
            skip_connections.append(x)
            x = self.pool(x)

        x = self.bottleneck(x)
        skip_connections = skip_connections[::-1]

        for idx in range(0, len(self.ups), 2):
            x = self.ups[idx](x)
            skip_connection = skip_connections[idx//2]

            if x.shape != skip_connection.shape:
                x = F.interpolate(x, size=skip_connection.shape[2:], mode='bilinear', align_corners=True)

            concat_skip = torch.cat((skip_connection, x), dim=1)
            x = self.ups[idx+1](concat_skip)

        return self.final_conv(x)

然后我们定义数据加载器：

class LungDataset(torch.utils.data.Dataset):
    def __init__(self, image_dir, mask_dir, transform=None):
        self.image_dir = image_dir
        self.mask_dir = mask_dir
        self.transform = transform
        self.images = os.listdir(image_dir)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, index):
        image_path = os.path.join(self.image_dir, self.images[index])
        mask_path = os.path.join(self.mask_dir, self.images[index])

        image = np.array(Image.open(image_path).convert('L'), dtype=np.float32)
        mask = np.array(Image.open(mask_path).convert('L'),