Paper
Unifying Multiple Foundation Models for Advanced Computational Pathology
Authors
Wenhui Lei, Yusheng Tan, Anqi Li, Hanyu Chen, Hengrui Tian, Ruiying Li, Zhengqun Jiang, Fang Yan, Xiaofan Zhang, Shaoting Zhang
Abstract
Foundation models have advanced computational pathology by learning transferable visual representations from large histological datasets, yet recent evaluations reveal substantial variability in their performance across tasks. This inconsistency arises from differences in training data diversity and is further constrained by the reliance of many high-performing models on proprietary datasets that cannot be shared or expanded. Offline distillation offers a partial remedy but depends heavily on the size and heterogeneity of the distillation corpus and requires full retraining to incorporate new models. To address these limitations, we propose Shazam, a task-specific online integration framework that unifies multiple pretrained pathology foundation models within a single flexible inference system. Shazam fuses multi-level representations through adaptive expert weighting and learns task-aligned features via online distillation. Across spatial transcriptomics prediction, survival prognosis, tile classification, and visual question answering, Shazam consistently outperforms strong individual models, highlighting its promise as a scalable approach for harnessing the rapid evolution of pathology foundation models in a unified and adaptable manner.