CPathAgent：一种模拟病理学家诊断逻辑、用于可解释高分辨率病理图像分析的基于智能体的基础模型
CPathAgent: An Agent-based Foundation Model for Interpretable High-Resolution Pathology Image Analysis Mimicking Pathologists' Diagnostic Logic
AI总结：
针对现有计算病理基础模型缺乏可解释性、无法模拟病理学家诊断逻辑的问题，提出基于智能体的CPathAgent，通过多阶段训练实现多尺度推理，构建PathMMU-HR2基准，在多尺度测试中性能优于现有方法。
摘要：
计算病理学的最新进展催生了众多基础模型。这些模型通常依赖带有多示例学习的通用编码器进行全切片图像（WSI）分类，或采用多模态方法直接从图像生成报告。然而，这些模型无法模拟病理学家的诊断方式——病理学家会先在低倍镜下系统观察切片以获取整体视图，再逐步放大可疑区域以形成综合诊断。相反，现有模型直接输出最终诊断结果，却不揭示背后的推理过程。为弥补这一空白，我们提出CPathAgent，这是一种创新的基于智能体的方法，它能根据观察到的视觉特征在WSI上自主导航，从而模拟病理学家的诊断工作流程，生成透明度和可解释性显著更高的诊断摘要。为实现这一目标，我们开发了一种多阶段训练策略，将 patch 级、区域级和WSI级的能力统一到单个模型中，这对于复现病理学家在不同图像尺度下理解和推理的方式至关重要。此外，我们构建了PathMMU-HR2，这是首个经过专家验证的大区域分析基准。它代表了patch与全切片之间一个关键的中间尺度，反映了一项重要的临床实际情况：病理学家通常一次检查几个关键的大区域，而非整个切片。大量实验表明，CPathAgent在三种不同图像尺度的基准测试中始终优于现有方法，验证了我们基于智能体的诊断方法的有效性，也为计算病理学指明了一个颇具前景的方向。
英文摘要：
Recent advances in computational pathology have led to the emergence of numerous foundation models. These models typically rely on general-purpose encoders with multi-instance learning for whole slide image (WSI) classification or apply multimodal approaches to generate reports directly from images. However, these models cannot emulate the diagnostic approach of pathologists, who systematically examine slides at low magnification to obtain an overview before progressively zooming in on suspicious regions to formulate comprehensive diagnoses. Instead, existing models directly output final diagnoses without revealing the underlying reasoning process. To address this gap, we introduce CPathAgent, an innovative agent-based approach that mimics pathologists' diagnostic workflow by autonomously navigating across WSI based on observed visual features, thereby generating substantially more transparent and interpretable diagnostic summaries. To achieve this, we develop a multi-stage training strategy that unifies patch-level, region-level, and WSI-level capabilities within a single model, which is essential for replicating how pathologists understand and reason across diverse image scales. Additionally, we construct PathMMU-HR2, the first expert-validated benchmark for large region analysis. This represents a critical intermediate scale between patches and whole slides, reflecting a key clinical reality where pathologists typically examine several key large regions rather than entire slides at once. Extensive experiments demonstrate that CPathAgent consistently outperforms existing approaches across benchmarks at three different image scales, validating the effectiveness of our agent-based diagnostic approach and highlighting a promising direction for computational pathology.
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。
