发表机构
Yonsei University; KAIST; Hallym University Chuncheon Sacred Heart Hospital（延世大学; 韩国科学技术院; 翰林大学春川圣心医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对数字病理学层次化紧凑证据评估中参考替换问题，提出CHARTER审计框架，通过声明目标与参考、量化预测偏移、审计比较稳定性，揭示并规范隐式选择，区分真实保持与表面增益。
AI 中文摘要
在数字病理学中，紧凑证据常被用于解释或审计全切片图像多实例学习模型的预测。在层次化紧凑证据流程中，候选过滤引入了策略特定的候选条件预测，与原始全包预测并存。然而，如果评估参考发生变化而预期目标仍是原始全包预测，那么不仅同一紧凑证据的实测保真度可能改变，竞争性候选策略之间的比较也可能改变。为使这种依赖性明确化，我们引入CHARTER，一种参考感知的评估章程，要求研究者声明预期目标和参考，量化候选引起的预测偏移，并审计比较结论的稳定性。在我们主要的五种子随机核审计的15项比较中，4项显示出确定性反转；在匹配的原生排名压力测试中，ACMIL比较从反转变为保持。CHARTER将原本隐式的候选过滤和参考选择转化为可审计的评估规范，有助于区分对预期预测的真实保持与因改变被解释预测而产生的表面增益。
英文摘要
In digital pathology, compact evidence is often used to explain or audit predictions made by whole-slide image multiple instance learning models. In hierarchical compact-evidence pipelines, candidate filtering introduces a strategy-specific candidate-conditioned prediction alongside the original full-bag prediction. If the evaluation reference changes while the intended target remains the original full-bag prediction, however, not only can the measured fidelity of the same compact evidence change, but comparisons between competing candidate strategies can also change. To make this dependence explicit, we introduce CHARTER, a reference-aware evaluation charter that asks researchers to DECLARE the intended target and reference, QUANTIFY candidate-induced prediction shift, and AUDIT the stability of comparative conclusions. Across the 15 comparisons in our main five-seed Random-K audit, 4 showed determinate reversals; in a matched native-ranking stress test, the ACMIL comparison changed from REVERSED to PRESERVED. CHARTER turns otherwise implicit candidate-filtering and reference choices into an auditable evaluation specification, helping distinguish genuine preservation of the intended prediction from apparent gains induced by changing the prediction being explained.
