Pathology foundation models (FMs) have become central to computational histopathology, offering strong transfer performance across a wide range of diagnostic and prognostic tasks. The rapid proliferation of pathology foundation models creates a model-selection bottleneck: no single model is uniformly best, yet exhaustively adapting and validating many candidates for each downstream endpoint is prohibitively expensive. We address this challenge with a lightweight and novel model fusion strategy, LogitProd, which treats independently trained FM-based predictors as fixed experts and learns sample-adaptive fusion weights over their slide-level outputs. The fusion operates purely on logits, requiring no encoder retraining and no feature-space alignment across heterogeneous backbones. We further provide a theoretical analysis showing that the optimal weighted product fusion is guaranteed to perform at least as well as the best individual expert under the training objective. We systematically evaluate LogitProd on \textbf{22} benchmarks spanning WSI-level classification, tile-level classification, gene mutation prediction, and discrete-time survival modeling. LogitProd ranks first on 20/22 tasks and improves the average performance across all tasks by ~3% over the strongest single expert. LogitProd enables practitioners to upgrade heterogeneous FM-based pipelines in a plug-and-play manner, achieving multi-expert gains with $\sim$12$\times$ lower training cost than feature-fusion alternatives.

翻译：病理基础模型已成为计算组织病理学的核心，在多种诊断和预后任务中展现出强大的迁移性能。然而，病理基础模型的快速扩散带来了模型选择瓶颈：没有单一模型能一贯最优，但针对每个下游终点穷尽性地适配和验证众多候选模型代价高昂。为此，我们提出一种轻量级的新型模型融合策略LogitProd，将独立训练的基于基础模型的预测器视为固定专家，并在其全切片级别输出上学习样本自适应融合权重。该融合仅在对数尺度上运行，无需重新训练编码器，也无需对齐异构骨干网络的特征空间。我们进一步提供理论分析，证明在训练目标下最优加权乘积融合的性能至少与最佳单个专家相当。我们在涵盖WSI级别分类、图块级别分类、基因突变预测和离散时间生存建模的22项基准任务上系统评估LogitProd。LogitProd在20/22项任务中排名第一，并在所有任务上将平均性能较最强单一专家提升约3%。LogitProd使从业者能够以即插即用的方式升级异构基础模型流水线，以比特征融合替代方案低约12倍的训练成本实现多专家增益。