SAGE用多智能体系统让病理生物标志物发现更可解释、可验证。
SAGE: Agentic Framework for Interpretable and Clinically Translatable Computational Pathology Biomarker Discovery
- 基于知识图谱和本体推理生成假说，避免盲目猜测
- 多智能体辩论机制评估新标志物的创新性与文献一致性
- 自动化流程实现从假设到多模态数据验证的端到端执行
基于图像的生物标志物为计算病理学提供了比黑箱AI更具临床可解释性的替代方案，但其发现仍主要依赖直觉，受零散文献引导而非严格的生物学验证。我们提出SAGE（结构化代理系统用于假设生成与评估），一种多智能体框架，通过三种机制将标志物发现建立在生物证据基础上：(i) 基于知识图谱锚定的多路径本体推理生成假说；(ii) 基于辩论的多智能体新颖性评估，对候选标志物进行现有文献压力测试；(iii) 端到端自动化验证流水线，将假说直接转化为多模态病理数据集上的可执行分析。这三者共同将标志物发现从依赖直觉、文献浏览的过程转变为可追溯、可审查的结构化推理流程，使临床医生和研究人员能够审视、信任并在此基础上继续推进。
原文摘要 · Abstract (English)
Engineered image-based biomarkers offer a clinically interpretable alternative to black-box AI in computational pathology, yet their discovery remains largely intuition-driven, guided by fragmented literature rather than rigorous biological validation. We introduce SAGE (Structured Agentic system for hypothesis Generation and Evaluation), a multi-agent framework that grounds biomarker discovery in biological evidence through three mechanisms: (i) knowledge-graph-anchored hypothesis generation via multi-path ontological reasoning, (ii) a debate-based multi-agent novelty assessment that stress-tests candidate biomarkers against existing literature, and (iii) an end-to-end automated validation pipeline that translates hypotheses directly into executable analyses on multimodal pathology datasets. Together, these components shift biomarker discovery from an intuition-driven, literature-browsing exercise into a structured, traceable reasoning process that clinicians and researchers can inspect, trust, and build upon.
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