简单智能体比专家更高效地优化生物医学图像处理流程
Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization
- 用简单智能体自动完成图像分析代码适配，避免复杂手动编程
- 在三个真实生物医学图像流程中，智能体生成代码性能超越人工专家
- 证明复杂智能体未必更好，为实际应用提供清晰设计路径
将生产级计算机视觉工具适配到特定科学数据集是关键的“最后一公里”难题。当前方案不切实际：微调需要大量标注数据，科学家往往缺乏；手动代码修改则需数周至数月。我们探索用AI智能体自动化这一过程，聚焦于该任务中智能体设计的最优方案。提出系统评估框架，对三个生产级生物医学成像流水线进行研究。结果表明，简单智能体框架持续生成的适配代码优于人类专家方案。分析显示，常见复杂智能体架构并非普遍有益，从而提出实用的智能体设计路线图。开源评估框架，并通过将智能体生成函数部署到生产流水线，验证方法可实现真实落地影响。
原文摘要 · Abstract (English)
Adapting production-level computer vision tools to bespoke scientific datasets is a critical "last mile" bottleneck. Current solutions are impractical: fine-tuning requires large annotated datasets scientists often lack, while manual code adaptation costs scientists weeks to months of effort. We consider using AI agents to automate this manual coding, and focus on the open question of optimal agent design for this targeted task. We introduce a systematic evaluation framework for agentic code optimization and use it to study three production-level biomedical imaging pipelines. We demonstrate that a simple agent framework consistently generates adaptation code that outperforms human-expert solutions. Our analysis reveals that common, complex agent architectures are not universally beneficial, leading to a practical roadmap for agent design. We open source our framework and validate our approach by deploying agent-generated functions into a production pipeline, demonstrating a clear pathway for real-world impact.
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