医学图像分类pytorch

对于医学图像分类，你可以使用PyTorch来实现。下面是一个简单的示例代码，展示了如何使用PyTorch构建一个基本的医学图像分类模型：

首先，导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models, transforms, datasets

然后定义数据预处理的transforms：

data_transforms = {
    'train': transforms.Compose([
        transforms.Resize(256),
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

接下来加载数据集：

data_dir = 'path_to_dataset_directory'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4) for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

定义模型：

model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, len(class_names))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)

定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

训练模型：

num_epochs = 10
for epoch in range(num_epochs):
    for phase in ['train', 'val']:
        if phase == 'train':
            model.train()
        else:
            model.eval()
        
        running_loss = 0.0
        running_corrects = 0
        
        for inputs, labels in dataloaders[phase]:
            inputs = inputs.to(device)
            labels = labels.to(device)
            
            optimizer.zero_grad()
            
            with torch.set_grad_enabled(phase == 'train'):
                outputs = model(inputs)
                _, preds = torch.max(outputs, 1)
                loss = criterion(outputs, labels)
                
                if phase == 'train':
                    loss.backward()
                    optimizer.step()
                    
            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds == labels.data)
            
        epoch_loss = running_loss / dataset_sizes[phase]
        epoch_acc = running_corrects.double() / dataset_sizes[phase]
        
        print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

这只是一个基本的示例，你可以根据自己的需求对代码进行修改和扩展。希望对你有帮助！