Existing computational pathology methods predominantly operate within whole-slide image (WSI)-level multiple instance learning (MIL) paradigms, while patient-level modeling remains underexplored. In routine pathological practice, however, pathologists derive diagnostic and prognostic conclusions by integrating evidence across multiple WSIs rather than relying on any single slide. This discrepancy creates a fundamental misalignment when patient-level supervision is directly imposed on conventional MIL frameworks, often leading to unstable optimization and degraded predictive reliability. To address this issue, we propose Anchor-Guided Evidence MIL (AGE-MIL), a weakly supervised framework for patient-level prediction. AGE-MIL constructs a patient-level anchor from slide representations to capture global pathological context and guide the retrieval and integration of diagnostically relevant local patches, enabling robust patient-level modeling. Patient-level risk is further modeled as an evidence accumulation process, promoting stable optimization under weak supervision. AGE-MIL is evaluated on six clinically relevant patient-level prediction tasks from two independent cohorts. Experimental results show that the proposed framework consistently outperforms eight state-of-the-art MIL methods. Code is available at https://github.com/wodeniua/AGE-MIL.

翻译：现有计算病理学方法主要在全切片图像层面的多实例学习范式下运行，而患者层面建模仍未得到充分探索。然而，在常规病理实践中，病理学家通过整合多个全切片图像中的证据（而非依赖单一切片）得出诊断和预后结论。当将患者层面监督直接施加于传统多实例学习框架时，这种差异会造成根本性错位，常常导致优化不稳定和预测可靠性下降。为解决该问题，我们提出锚点引导证据多实例学习（AGE-MIL），一种用于患者层面预测的弱监督框架。AGE-MIL从切片表征构建患者层面锚点，以捕获全局病理上下文并引导诊断相关局部斑块的检索与整合，从而实现稳健的患者层面建模。患者层面风险进一步建模为证据积累过程，在弱监督下促进稳定优化。AGE-MIL在两个独立队列的六个临床相关患者层面预测任务上进行了评估。实验结果表明，所提框架持续优于八种最先进的多实例学习方法。代码见https://github.com/wodeniua/AGE-MIL。