CellDX AI Autopilot：基于代理的训练与部署病理分类器
CellDX AI Autopilot: Agent-Guided Training and Deployment of Pathology Classifiers
AI总结：
CellDX AI Autopilot通过自然语言交互让非ML背景的病理学家和ML专家高效训练、评估和部署病理分类器，利用预构建的32000+病例和66000张H&E染色全切片图像数据集，结合多实例学习框架和迭代双变量超参数搜索，实现端到端自动化模型训练。
摘要：
训练计算病理学AI模型目前需要昂贵的全切片图像数据集、GPU基础设施、深度机器学习专业知识和大量工程努力。我们提出了CellDX AI Autopilot平台，允许用户从无ML背景的病理学家到运行多个并行实验的ML专家，通过与AI代理的自然语言交互来训练、评估和部署全切片图像分类器。该平台提供了一套结构化的代理技能，引导用户通过数据集整理、自动超参数调优、多策略模型比较和人机协作部署，所有操作均基于预构建的包含32,000多病例和66,000张H&E染色全切片图像的数据集。我们描述了代理技能架构、支持四种分类策略的多实例学习（MIL）训练框架，以及一种迭代的成对超参数搜索（网格或种子随机）方法，将调优成本降低超过30倍相比穷举搜索。CellDX AI Autopilot是目前已知首个将病理专业化的代理技能和训练平台暴露给通用AI代理（例如任何基于LLM的代理运行时）的系统，实现端到端自动化模型训练，而无需代理本身具备领域专业知识。该平台解决了限制诊断病理学应用的ML专业知识瓶颈，以及限制研究人员能有效运行多少实验的工程瓶颈。
英文摘要：
Training AI models for computational pathology currently requires access to expensive whole-slide-image datasets, GPU infrastructure, deep expertise in machine learning, and substantial engineering effort. We present CellDX AI Autopilot, a platform that lets users -- from pathologists with no ML background to ML practitioners running many parallel experiments -- train, evaluate, and deploy whole-slide image classifiers through natural language interaction with an AI agent. The platform provides a structured set of agent skills that guide the user through dataset curation, automated hyperparameter tuning, multi-strategy model comparison, and human-in-the-loop deployment, all on a pre-built dataset of over 32,000 cases and 66,000 H&E-stained whole-slide images with pre-extracted features. We describe the agent skill architecture, the underlying Multiple Instance Learning (MIL) training framework supporting four classification strategies, and an iterative pairwise hyperparameter search (grid or seeded random) that reduces tuning cost by over 30x compared to exhaustive search. CellDX AI Autopilot is, to our knowledge, the first system to expose pathology-specialized agent skills and a pathology-specialized training platform to general-purpose AI agents (e.g. any LLM-based agent runtime), delivering end-to-end automated model training without requiring the agent itself to be domain-specific. The platform addresses both the ML-expertise bottleneck that limits adoption in diagnostic pathology and the engineering bottleneck that limits how many experiments a researcher can run cost-effectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
