通过在线融合多模型，提升病理图像分析的准确性和扩展性。
Unifying Multiple Foundation Models for Advanced Computational Pathology
- 采用自适应加权融合多层级特征，实现模型在线集成。
- 在多种任务中优于单一模型，显著提升预测性能。
- 无需额外预训练，适合快速整合新模型与数据。
基础模型通过大规模病理数据学习可迁移的视觉表征，显著推动计算病理学发展，但其性能因训练数据差异和依赖专有数据集而波动较大。现有离线蒸馏方法虽部分缓解此问题，但需专用蒸馏数据并重复训练以集成新模型。本文提出Shazam，一种在线集成模型，可在统一且可扩展的表示学习框架中自适应融合多个预训练病理基础模型。研究发现，通过自适应专家加权与在线蒸馏融合多级特征，可高效整合各模型互补优势，无需额外预训练。在空间转录组预测、生存预后、切片级分类及视觉问答任务中，Shazam均持续超越强基线模型，证明在线模型集成是推进计算病理学的一种实用且可扩展策略。
原文摘要 · Abstract (English)
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.
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