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MCoT在医疗AI工程化编程的实践手册（下）

news 2025/10/28 6:37:07

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4.3 编程语言与框架选择

粗粒度分类器： Python + FastAPI/Flask。快速开发，易于在CPU上进行批量处理。
细粒度分析器： C++ with CUDA。3D UNet等大型模型在CPU上不可行，必须利用GPU加速能力。ONNX Runtime可以在这里部署。
单细胞多组学： Python + Scanpy/Anndata。这部分是研究性、探索性的，对延迟不敏感，但对Python生态依赖性强，适合在云端进行。
资源编排与监控： Go。编写一个轻量级的调度器，根据策略和当前负载，将任务路由到不同的执行环境（边缘GPU节点、云GPU集群）。

4.4 核心代码实现

A. 路由器/调度器

// router.go
package mainimport ("context""encoding/json""fmt"
)// Simplified structures
type InferenceJob struct {JobID       string `json:"job_id"`DataURI     string `json:"data_uri"`PipelineID  string `json:"pipeline_id"`
}type CoarseResult struct {JobID        string  `json:"job_id"`IsAbnormal   bool    `json:"is_abnormal"`Probability  float64 `json:"probability"`
}func main() {// A message queue consumerfor job := range getJobsFromQueue("new_inference_queue") {// 1. Dispatch to Coarse Classifier (Python via gRPC/HTTP)coarseResult := callCoarseClassifier(job)// 2. Apply cascade policypolicy := getCascadePolicy(job.PipelineID)isTriggered := evaluatePolicy(coarseResult, policy)if isTriggered {// 3. Dispatch to Fine Analyzer (C++ via gRPC)fmt.Printf("Job %s: Abnormal detected (p=%f). Dispatching to fine analyzer.\n", job.JobID, coarseResult.Probability)callFineAnalyzer(job)} else {// 4. Mark as complete and store coarse resultfmt.Printf("Job %s: Normal. Finishing.\n", job.JobID)storeFinalResult(job.JobID, coarseResult)}}
}func evaluatePolicy(result CoarseResult, policy map[string]string) bool {// Very simple policy evaluation, in reality this would be more robusttriggerRule, ok := policy["coarse_to_fine_trigger"]if !ok {return false}// In a real system, use a simple expression parser// Here we assume "abnormal_prob > 0.1"return result.Probability > 0.1
}

B. C++ 细粒度分析器

// fine_analyzer.cpp (using ONNX Runtime with CUDA GPU)
#include <onnxruntime_cxx_api.h>
#include <cuda_provider_factory.h>void run_fine_analysis(const std::string& job_id, const std::string& data_uri) {// 1. Setup environment with CUDAOrt::Env env(ORT_LOGGING_LEVEL_WARNING, "FineAnalyzer");Ort::SessionOptions session_options;Ort::ThrowOnError(OrtSessionOptionsAppendExecutionProvider_CUDA(session_options, 0));Ort::Session session(env, L"unet3d_v2.onnx", session_options);// 2. Load large 3D volume data (e.g., a whole slide image patch)// This is memory intensive and a key reason for using C++auto input_tensor = load_and_preprocess_3d_volume(data_uri);// 3. Run inferenceauto output_tensors = session.Run(Ort::RunOptions{nullptr}, input_names.data(), &input_tensor