细胞通信级别的病理基础模型可解释性：基于微环境图蒸馏
Cellular-Communication-Level Interpretability for Pathology Foundation Models via Graph Distillation on Microenvironment
浏览论文内容
中文总结 AI 辅助
针对病理基础模型在细胞微环境尺度难以解释的问题，提出图蒸馏框架G-Interp，通过图神经网络学生蒸馏教师嵌入并生成细胞通信级解释，在多个组织病理学任务中实现可扩展且忠实的解释，同时保持预测性能。
中文摘要 AI 辅助
病理基础模型（PFMs）提供了强大的切片级表示，但在支撑临床推理的细胞和微环境尺度上仍难以解释。我们提出了图解释器（Graph-Interpreter, G-Interp），一种图蒸馏框架，为冻结的PFM教师模型配备一个细胞通信级别的“即插即用”解释器，而无需修改教师模型。对于每个切片，我们将细胞分割为图节点，并基于空间邻接构建微环境图。图神经网络（GNN）学生模型蒸馏PFM嵌入，同时学习基于注意力的消息传递，从而获得节点级和边级重要性。我们将这些重要性解释为细胞间通信证据，提供关于PFM如何编码微环境上下文的细粒度解释。为了在图抽象不完美时稳定蒸馏，我们采用一个轻量级辅助学生模型来提供补充视觉线索并调节图消息传递，同时保持主要可解释性信号源自图。我们通过将图选择的证据使用实例掩码映射回图像，并测量教师模型在定向与非定向遮挡下的敏感性来评估解释的忠实性。在多个组织病理学任务中，G-Interp产生了高度可扩展、微环境感知的解释，同时保持了有竞争力的预测性能。
英文摘要
Pathology foundation models (PFMs) provide strong tile-level representations but remain difficult to interpret at the cellular and microenvironmental scales that underpin clinical reasoning. We introduce Graph-Interpreter (G-Interp), a graph-distillation framework that equips a frozen PFM teacher with a cellular-communication-level "plug-in" interpreter, without modifying the teacher. For each tile, we segment cells as graph nodes and construct a microenvironment graph based on spatial adjacency. Graph neural network (GNN) students distil the PFM embedding, whilst learning attention-based message passing that yields node- and edge-level importances. We interpret these importances as cell-cell communication evidence, providing fine-grained explanations of how PFMs encode microenvironmental context. To stabilise distillation when graph abstraction is imperfect, we employ a lightweight auxiliary student to supply complementary visual cues and condition graph message passing, while keeping the primary interpretability signal graph-derived. We evaluate explanation faithfulness by mapping graph-selected evidence back to the image using instance masks and measuring teacher sensitivity under targeted vs non-target occlusions. Across multiple histopathology tasks, G-Interp produces highly scalable, microenvironment-aware explanations, while maintaining competitive predictive performance.
发表机构
- South China University of Technology（华南理工大学）
- University of Leicester（莱斯特大学）
- University of Oxford（牛津大学）
- Aston University（阿斯顿大学）
- ZoyMed
机构由 AI 辅助整理，请以论文原文为准。
