NOVA让AI自动完成病理分析，从提问到发现一气呵成
NOVA: An Agentic Framework for Automated Histopathology Analysis and Discovery
- 基于迭代生成代码的智能代理框架，自动执行病理分析流程
- 在90问基准测试中超越现有模型，成功关联形态特征与预后亚型
- 适合临床研究者、病理学家，助力自动化医学发现
数字化病理分析流程复杂且耗时，依赖专业人才，限制了其广泛应用。我们提出NOVA，一个智能代理框架，能将科学问题转化为可执行的分析流水线，通过不断生成并运行Python代码实现。NOVA集成49个基于开源软件的领域专用工具（如核分割、全切片编码），也可临时创建新工具。为评估此类系统，我们构建了SlideQuest——一个由病理科医生和生物医学科学家验证的90题基准，涵盖数据处理、定量分析和假设检验。与以往侧重知识记忆或诊断问答的生物医学基准不同，SlideQuest要求多步推理、迭代编程和计算求解。定量评估显示，NOVA优于现有编码代理基线；一项经病理科医生验证的案例研究成功将组织形态与预后相关的PAM50亚型关联，证明其具备规模化发现潜力。
原文摘要 · Abstract (English)
Digitized histopathology analysis involves complex, time-intensive workflows and specialized expertise, limiting its accessibility. We introduce NOVA, an agentic framework that translates scientific queries into executable analysis pipelines by iteratively generating and running Python code. NOVA integrates 49 domain-specific tools (e.g., nuclei segmentation, whole-slide encoding) built on open-source software, and can also create new tools ad hoc. To evaluate such systems, we present SlideQuest, a 90-question benchmark -- verified by pathologists and biomedical scientists -- spanning data processing, quantitative analysis, and hypothesis testing. Unlike prior biomedical benchmarks focused on knowledge recall or diagnostic QA, SlideQuest demands multi-step reasoning, iterative coding, and computational problem solving. Quantitative evaluation shows NOVA outperforms coding-agent baselines, and a pathologist-verified case study links morphology to prognostically relevant PAM50 subtypes, demonstrating its scalable discovery potential.
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