We introduce ReaMIL (Reasoning- and Evidence-Aware MIL), a multiple instance learning approach for whole-slide histopathology that adds a light selection head to a strong MIL backbone. The head produces soft per-tile gates and is trained with a budgeted-sufficiency objective: a hinge loss that enforces the true-class probability to be $\geq τ$ using only the kept evidence, under a sparsity budget on the number of selected tiles. The budgeted-sufficiency objective yields small, spatially compact evidence sets without sacrificing baseline performance. Across TCGA-NSCLC (LUAD vs. LUSC), TCGA-BRCA (IDC vs. Others), and PANDA, ReaMIL matches or slightly improves baseline AUC and provides quantitative evidence-efficiency diagnostics. On NSCLC, it attains AUC 0.983 with a mean minimal sufficient K (MSK) $\approx 8.2$ tiles at $τ= 0.90$ and AUKC $\approx 0.864$, showing that class confidence rises sharply and stabilizes once a small set of tiles is kept. The method requires no extra supervision, integrates seamlessly with standard MIL training, and naturally yields slide-level overlays. We report accuracy alongside MSK, AUKC, and contiguity for rigorous evaluation of model behavior on WSIs.

翻译：我们提出了ReaMIL（推理与证据感知多实例学习），一种面向全切片组织病理学的多实例学习方法，该方法在强性能多实例学习骨干网络上增加了一个轻量级选择头。该选择头生成每个样本块的软门控，并通过预算充分性目标进行训练：在选定样本块数量的稀疏预算约束下，使用保留的证据强制真实类别概率≥ τ的合页损失。该预算充分性目标能够在不牺牲基线性能的前提下，生成紧凑的空间连续证据集。在TCGA-NSCLC（LUAD vs. LUSC）、TCGA-BRCA（IDC vs. Others）和PANDA数据集上，ReaMIL匹配或略微提升基线AUC，并提供定量证据效率诊断。在NSCLC上，该方法在τ=0.90时达到AUC 0.983，平均最小充分样本块数（MSK）≈8.2，AUKC≈0.864，表明类别置信度在保留少量样本块后迅速上升并趋于稳定。该方法无需额外监督，可与标准多实例学习训练无缝集成，并自然生成切片级覆盖图。我们报告准确性及MSK、AUKC和连续性指标，以对全切片图像上的模型行为进行严格评估。