Multiple Instance Learning (MIL) is a standard paradigm for Whole-Slide Image (WSI) analysis and has achieved strong results in computational pathology. However, most MIL pipelines assume a single "gold" label per slide, which conflicts with clinical practice where substantial inter-pathologist variability is common. Existing multi-annotator learning and label-refinement methods typically estimate global annotator reliability or rely on single-instance assumptions, making them poorly suited to MIL and to localized diagnostic contexts where experts disagree. We propose RaLMPH (Reliability-aware Learning for Multi-Pathologist Harmonization), a MIL-based label reconciliation framework for WSIs annotated by multiple pathologists. RaLMPH introduces a reliability field that jointly models (i) local neighborhood structure in WSI feature space and (ii) expert uncertainty (entropy), enabling per-sample identification of trustworthy reference neighborhoods. Leveraging this field, RaLMPH performs sample-wise local annotator ranking to select reliable opinions per slide and applies an adaptive gating mechanism to fuse labels conditioned on local reliability. Experiments on a clinical WSI dataset with labels from six pathologists, as well as controlled simulated benchmarks, show that RaLMPH consistently outperforms existing approaches. Further analyses clarify how our reliability-aware mechanism improves label reconciliation and downstream MIL performance.

翻译：多示例学习（MIL）是进行全切片图像（WSI）分析的标准范式，并在计算病理学领域取得了显著成果。然而，大多数MIL流程假设每张切片存在单一的"金标准"标签，这与临床实践中常见的病理学家间显著变异相矛盾。现有方法（包括多标注者学习和标签修正技术）通常估计全局标注者可靠性或依赖单示例假设，因而难以适应MIL场景以及专家意见存在分歧的局部诊断环境。本文提出RaLMPH（面向多病理学家一致性的可靠性感知学习），一种基于MIL的多病理学家标注切片标签协调框架。RaLMPH引入可靠性场，该场联合建模（i）WSI特征空间中的局部邻域结构与（ii）专家不确定性（熵），实现每张切片上可信参考邻域的识别。利用该可靠性场，RaLMPH执行逐样本局部标注者排序以选取每张切片的可靠意见，并采用自适应门控机制基于局部可靠性融合标签。在包含六位病理学家标注的临床WSI数据集及受控模拟基准上的实验表明，RaLMPH始终优于现有方法。进一步分析阐明了我们的可靠性感知机制如何改善标签协调与下游MIL性能。