用SQL记录病理分析决策过程，让模型推理可审计可验证。
Toward Auditable Neuro-Symbolic Reasoning in Pathology: SQL as an Explicit Trace of Evidence
- 以SQL为线索，将细胞特征转化为可执行的查询语句
- 在两个数据集上提升可解释性与诊断路径透明度
- 适合需要可追溯医疗决策的临床研究与监管场景
自动化病理图像分析对临床诊断至关重要，但医生仍质疑模型决策依据。现有视觉语言模型虽能生成自然语言解释，但多为相关性描述，缺乏可验证证据。本文提出一种以SQL为核心的智能体框架，通过提取可读的细胞特征，由特征推理智能体生成并执行SQL查询，将视觉证据聚合为量化结论。知识比对智能体则将这些结论与已知病理知识对照，模拟病理科医生基于可观测数据的诊断逻辑。在两个病理视觉问答数据集上的实验表明，该方法显著提升了可解释性与决策可追溯性，且生成了可执行的SQL追踪链，实现从细胞测量到诊断结论的全程可审计。
原文摘要 · Abstract (English)
Automated pathology image analysis is central to clinical diagnosis, but clinicians still ask which slide features drive a model's decision and why. Vision-language models can produce natural language explanations, but these are often correlational and lack verifiable evidence. In this paper, we introduce an SQL-centered agentic framework that enables both feature measurement and reasoning to be auditable. Specifically, after extracting human-interpretable cellular features, Feature Reasoning Agents compose and execute SQL queries over feature tables to aggregate visual evidence into quantitative findings. A Knowledge Comparison Agent then evaluates these findings against established pathological knowledge, mirroring how pathologists justify diagnoses from measurable observations. Extensive experiments evaluated on two pathology visual question answering datasets demonstrate our method improves interpretability and decision traceability while producing executable SQL traces that link cellular measurements to diagnostic conclusions.
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