Foundation models have substantially advanced computational pathology by learning transferable visual representations from large histological datasets, yet their performance varies widely across tasks due to differences in training data composition and reliance on proprietary datasets that cannot be cumulatively expanded. Existing efforts to combine foundation models through offline distillation partially mitigate this issue but require dedicated distillation data and repeated retraining to integrate new models. Here we present Shazam, an online integration model that adaptively combines multiple pretrained pathology foundation models within a unified and scalable representation learning paradigm. Our findings show that fusing multi-level features through adaptive expert weighting and online distillation enables efficient consolidation of complementary model strengths without additional pretraining. Across spatial transcriptomics prediction, survival prognosis, tile-level classification, and visual question answering, Shazam consistently outperforms strong individual models, demonstrating that online model integration provides a practical and extensible strategy for advancing computational pathology.

翻译：基础模型通过从大规模组织学数据集中学习可迁移的视觉表征，显著推动了计算病理学的发展。然而，由于训练数据构成的差异以及对无法持续扩展的专有数据集的依赖，这些模型在不同任务上的性能表现差异显著。现有通过离线蒸馏融合基础模型的方法部分缓解了这一问题，但需要专门的蒸馏数据并需反复重新训练以整合新模型。本文提出Shazam，一种在线集成模型，可在统一且可扩展的表征学习范式内自适应地结合多个预训练病理学基础模型。我们的研究结果表明，通过自适应专家加权和在线蒸馏融合多层次特征，能够有效整合互补模型优势而无需额外预训练。在空间转录组预测、生存预后、组织切片级分类及视觉问答等任务中，Shazam均持续优于各独立强基线模型，证明在线模型集成为推进计算病理学提供了实用且可扩展的策略。