BioFuse: an embedding fusion framework for biomedical foundation models
Mirza Nasir Hossain, David Harris-Birtill, Xu Yanwu, Xu Yanwu, Xu Yanwu, Xu Yanwu
TL;DR
BioFuse is a new framework that combines biomedical foundation models to improve performance and uncover cross-modal relationships in data.
Contribution
BioFuse introduces a novel embedding fusion framework using multiple pre-trained models and grid search for optimal combinations.
Findings
BioFuse achieves state-of-the-art AUC in 5/12 datasets on the MedMNIST+ benchmark.
The framework demonstrates unexpected cross-modal capabilities across imaging modalities.
BioFuse offers an extensible architecture for future model integration and fusion techniques.
Abstract
The biomedical field has witnessed a surge in pre-trained foundation models excelling in specific subdomains such as radiology and histopathology. While integrating these models promises a more comprehensive understanding of biomedical data, it poses challenges in model compatibility and feature fusion. We present BioFuse, a novel open-source framework designed to generate optimised biomedical embeddings. BioFuse utilises a pool of 9 state-of-the-art (SOTA) foundation models to create task-specific embeddings. It employs grid search to automatically identify the optimal combination of models, fusing their embeddings through vector concatenation. On the MedMNIST+ benchmark, using XGBoost as the downstream classifier, BioFuse outperforms several existing methods, achieving SOTA AUC in 5/12 datasets, while maintaining near-SOTA performance across most remaining datasets. Remarkably, our…
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Taxonomy
TopicsAI in cancer detection · Medical Imaging and Analysis · COVID-19 diagnosis using AI