AI大语言模型在医学影像诊断中的应用探讨

    import cv2
    import numpy as np
    
    def preprocess_image(image):
    # 去噪
    denoised_image = cv2.fastNlMeansDenoising(image)
    
    # 归一化
    normalized_image = (denoised_image - np.min(denoised_image)) / (np.max(denoised_image) - np.min(denoised_image))
    
    # 切割
    cropped_image = normalized_image[50:450, 50:450]
    
    return cropped_image
    
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import numpy as np
    
    def extract_features(image):
    # 将影像数据划分为多个局部区域
    patches = []
    patch_size = 32
    stride = 16
    for i in range(0, image.shape[0] - patch_size, stride):
        for j in range(0, image.shape[1] - patch_size, stride):
            patch = image[i:i+patch_size, j:j+patch_size]
            patches.append(patch)
    
    # 将局部区域的特征表示为一个序列
    features = np.array(patches).reshape(len(patches), -1)
    
    return features
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    import torch
    from transformers import GPT2Model, GPT2Config
    from torch.utils.data import DataLoader, TensorDataset
    from sklearn.model_selection import train_test_split
    
    # 加载数据
    X = np.load("features.npy")
    y = np.load("labels.npy")
    
    # 划分训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # 转换为PyTorch张量
    X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
    y_train_tensor = torch.tensor(y_train, dtype=torch.long)
    X_test_tensor = torch.tensor(X_test, dtype=torch.float32)
    y_test_tensor = torch.tensor(y_test, dtype=torch.long)
    
    # 创建数据加载器
    train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
    test_dataset = TensorDataset(X_test_tensor, y_test_tensor)
    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
    
    # 创建AI大语言模型
    config = GPT2Config()
    model = GPT2Model(config)
    
    # 创建分类器
    classifier = torch.nn.Linear(config.n_embd, num_classes)
    
    # 定义损失函数和优化器
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
    
    # 训练模型
    for epoch in range(10):
    model.train()
    for batch in train_loader:
        inputs, labels = batch
    
        # 前向传播
        outputs = model(inputs)
        logits = classifier(outputs.last_hidden_state[:, -1, :])
    
        # 计算损失
        loss = criterion(logits, labels)
    
        # 反向传播
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    
    # 评估模型
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for batch in test_loader:
            inputs, labels = batch
    
            # 前向传播
            outputs = model(inputs)
            logits = classifier(outputs.last_hidden_state[:, -1, :])
    
            # 计算准确率
            _, predicted = torch.max(logits.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    
    print("Epoch: {}, Accuracy: {:.2f}%".format(epoch+1, 100 * correct / total))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读