Shazam: Unifying Multiple Foundation Models for Advanced Computational Pathology
Journal:
arXiv
Published Date:
Mar 2, 2025
Abstract
Foundation Models (FMs) in computational pathology (CPath) have significantly
advanced the extraction of meaningful features from histopathology image
datasets, achieving strong performance across various clinical tasks. Despite
their impressive performance, these models often exhibit variability when
applied to different tasks, prompting the need for a unified framework capable
of consistently excelling across various applications. In this work, we propose
Shazam, a novel framework designed to efficiently combine multiple CPath
models. Unlike previous approaches that train a fixed-parameter FM, Shazam
dynamically extracts and refines information from diverse FMs for each specific
task. To ensure that each FM contributes effectively without dominance, a novel
distillation strategy is applied, guiding the student model with features from
all teacher models, which enhances its generalization ability. Experimental
results on two pathology patch classification datasets demonstrate that Shazam
outperforms existing CPath models and other fusion methods. Its lightweight,
flexible design makes it a promising solution for improving CPath analysis in
real-world settings. Code will be available at
https://github.com/Tuner12/Shazam.
