医学图像二分类pytorch
时间: 2023-10-17 17:06:52 浏览: 52
对于医学图像的二分类任务,你可以使用PyTorch来实现。下面是一个简单的示例代码,以帮助你入门:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
# 定义自定义的数据集类
class MedicalImageDataset(torch.utils.data.Dataset):
def __init__(self, data, targets):
self.data = data
self.targets = targets
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
image = self.data[idx]
target = self.targets[idx]
return image, target
# 定义模型类
class Classifier(nn.Module):
def __init__(self):
super(Classifier, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU()
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(16 * 28 * 28, 64)
self.fc2 = nn.Linear(64,2)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.pool(x)
x = x.view(x.size(0), -1)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
# 准备数据
train_data = ...
train_targets = ...
dataset = MedicalImageDataset(train_data, train_targets)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# 初始化模型、损失函数和优化器
model = Classifier()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
for images, targets in dataloader:
optimizer.zero_grad()
output = model(images)
loss = criterion(output, targets)
loss.backward()
optimizer.step()
# 使用模型进行预测
test_data = ...
test_targets = ...
test_dataset = MedicalImageDataset(test_data, test_targets)
test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, targets in test_dataloader:
output = model(images)
_, predicted = torch.max(output.data, 1)
total += targets.size(0)
correct += (predicted == targets).sum().item()
accuracy = correct / total
print("Accuracy: {:.2f}%".format(accuracy * 100))
```