PathNavigate: 一种无需训练的病理学代理，具有惊喜引导扫描和共享幻灯片记忆用于全切片图像VQA
PathNavigate: A Training-Free Pathology Agent with Surprise-Guided Scan and Shared Slide Memory for Whole-Slide Image VQA
AI总结：
提出PathNavigate，一种无需训练的病理学代理，通过惊喜引导扫描和共享幻灯片记忆，在严格检查预算下高效定位证据并回答临床查询。
摘要：
全切片图像视觉问答（WSI-VQA）将病理学视为极端上下文搜索问题：为了回答自由形式的临床查询，系统必须首先在严格的检查预算下导航千兆像素切片，以定位稀疏的高分辨率证据。现有方法主要分为两种范式：i）监督式病理学多模态大语言模型（MLLMs）和代理可以将定位和推理吸收到学习模块中，但它们通常将导航与任务特定的监督和重新训练耦合，限制了其实用性；ii）无需训练的病理学代理通过保持核心模型冻结来避免这种成本，但通常遵循问题优先的设计，主要从查询条件相关性构建初始候选集。这可能会遗漏问题中未提及的决定性形态，并迫使更重的推理时脚手架。为了解决这一挑战，我们引入了PathNavigate，一种无需训练的病理学代理，基于扫描-搜索-读出流程构建。在问题匹配之前，PathNavigate在低放大倍数下扫描当前切片，使用共享的在线记忆模块处理冻结的病理学特征，生成一个切片特定的惊喜场，标记异常区域池。然后，它仅在此池内应用问题条件的PLIP相关性，以选择高放大倍数的搜索目标。最后，它提取局部高放大倍数证据，并使用冻结的感知器-裁决器堆栈进行回答，利用相同的在线记忆作为切片级上下文。在WSI-VQA和SlideBench-BCNB上的实验表明，所提出的扫描-搜索-读出设计提高了答案准确性，并产生了更可解释的证据选择轨迹，且效率更高。代码已在线公开。
英文摘要：
Whole-slide image visual question answering (WSI-VQA) frames pathology as an extreme-context search problem: to answer a free-form clinical query, a system must first navigate a gigapixel slide under a strict inspection budget to locate sparse, high-resolution evidence. Existing approaches largely fall into two paradigms: i) supervised pathology multimodal large language models (MLLMs) and agents can absorb localization and reasoning into learned modules, but they often couple navigation to task-specific supervision and retraining, limiting their practicality; ii) training-free pathology agents avoid this cost by keeping core models frozen, but often follow a question-first design, constructing the initial candidate set mainly from query-conditioned relevance. This can miss decisive morphology that is not named in the question, and force heavier inference-time scaffolding. To address this challenge, we introduce PathNavigate, a training-free pathology agent built around a scan-search-readout routine. Before question matching, PathNavigate scans the current slide at low magnification with a shared online memory module over frozen pathology features, producing a slide-specific surprise field that marks an abnormal-region pool. It then applies question-conditioned PLIP relevance only within this pool to select high-magnification search targets. Finally, it extracts local high-magnification evidence and answers with a frozen perceptor-adjudicator stack, using the same online memory as slide-level context. Experiments on WSI-VQA and SlideBench-BCNB show that the proposed scan-search-readout design improves answer accuracy and yields more interpretable evidence-selection trajectories with higher efficiency.The code is available online.
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