AI+医疗：基于深度学习的医学影像诊断系统

    from monai.networks.blocks import UnetrBasicBlock
    from monai.networks.nets import SwinUNETR
    
    class HybridSwinUNETR(nn.Module):
    def __init__(self, img_size=(96,96,96)):
        super().__init__()
        self.backbone = SwinUNETR(
            img_size=img_size,
            in_channels=1,
            out_channels=2,
            feature_size=48,
            use_checkpoint=True
        )
        self.crf_refinement = ConditionalRandomField(
            num_features=64, 
            iterations=5
        )
    
    def forward(self, x):
        x = self.backbone(x)
        x = self.crf_refinement(x)
        return x
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    class DynamicFeatureSelection(nn.Module):
    def __init__(self, in_channels, reduction=16):
        super().__init__()
        self.channel_attention = nn.Sequential(
            nn.AdaptiveAvgPool3d(1),
            nn.Conv3d(in_channels, in_channels//reduction, 1),
            nn.ReLU(),
            nn.Conv3d(in_channels//reduction, in_channels, 1),
            nn.Sigmoid()
        )
        self.spatial_attention = nn.Sequential(
            nn.Conv3d(in_channels, 1, 1),
            nn.Sigmoid()
        )
    
    def forward(self, x):
        # 通道注意力
        ca = self.channel_attention(x)
        # 空间注意力
        sa = self.spatial_attention(x)
        # 动态融合
        return x * ca * sa
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    # configs/train.yaml
    training:
      precision: 'bf16-mixed'
      gradient_clipping: 1.0
      optimizer:
    type: 'AdamW'
    lr: 1e-4
    weight_decay: 0.05
      scheduler:
    type: 'CosineAnnealingWarmRestarts'
    T_0: 10
    T_mult: 2
      early_stopping:
    monitor: 'val_dice'
    patience: 15
    mode: 'max'
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    # 启动8卡训练（NVIDIA A100）
    torchrun --nnodes=1 --nproc_per_node=8 \
    --rdzv_backend=c10d \
    --rdzv_endpoint=localhost:29500 \
    train.py --config configs/distributed.yaml
    
    
      
      
      
      
      
    

    def compute_metrics(y_true, y_pred):
    metrics = {
        'Dice': dice_score(y_true, y_pred),
        'Sensitivity': sensitivity(y_true, y_pred),
        'Specificity': specificity(y_true, y_pred),
        'HD95': hausdorff_distance(y_true, y_pred, percentile=95),
        'PPV': positive_predictive_value(y_true, y_pred),
        'NPV': negative_predictive_value(y_true, y_pred)
    }
    return metrics
    
    
      
      
      
      
      
      
      
      
      
      
    

    import triton_python_backend_utils as pb_utils
    
    class TritonModel:
    def execute(self, requests):
        responses = []
        for request in requests:
            # 获取输入张量
            input_tensor = pb_utils.get_input_tensor_by_name(request, "INPUT")
            ct_volume = input_tensor.as_numpy()
            
            # 预处理流水线
            processed = self.preprocess(ct_volume)
            
            # 模型推理
            output = self.model(processed)
            
            # 后处理与格式化
            mask = self.postprocess(output)
            
            # 构建响应
            out_tensor = pb_utils.Tensor("OUTPUT", mask)
            responses.append(pb_utils.InferenceResponse([out_tensor]))
        return responses
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    # 关键监控指标
    api_request_duration_seconds_bucket{handler="/predict", le="0.1"} 324
    gpu_memory_usage_percent{device="0"} 68.3
    model_inference_latency_ms{model="lung_seg"} 142
    data_drift_score 0.12
    
    
      
      
      
      
      
    

    class ReportGenerator:

    def __init__(self, llm_path="BioBERT"):
        self.llm = AutoModelForSeq2SeqLM.from_pretrained(llm_path)
        self.tokenizer = AutoTokenizer.from_pretrained(llm_path)
    
    def generate_report(self, findings):
        prompt = f"根据以下影像特征生成结构化报告：{findings}"
        inputs = self.tokenizer(prompt, return_tensors="pt")
        outputs = self.llm.generate(**inputs, max_length=512)
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)