Benchmarking pathology foundation models: Adaptation strategies and scenarios

Abstract

In computational pathology, several foundation models have recently developed, demonstrating enhanced learning capability for analyzing pathology images. However, adapting these models to various downstream tasks remains challenging, particularly when faced with datasets from different sources and acquisition conditions, as well as limited data availability. In this study, we benchmark four pathology-specific foundation models across 20 datasets and two scenarios - consistency assessment and flexibility assessment - addressing diverse adaptation scenarios and downstream tasks. In the consistency assessment scenario, involving five fine-tuning methods, we found that the parameter-efficient fine-tuning approach was both efficient and effective for adapting pathology-specific foundation models to diverse datasets within the same classification tasks. For slide-level survival prediction, the performance of foundation models depended on the choice of feature aggregation mechanisms and the characteristics of data. In the flexibility assessment scenario under data-limited environments, utilizing five few-shot learning methods, we observed that the foundation models benefited more from the few-shot learning methods that involve modification during the testing phase only. These findings provide insights that could guide the deployment of pathology-specific foundation models in real clinical settings, potentially improving the accuracy and reliability of pathology image analysis. The code for this study is available at https://github.com/QuIIL/BenchmarkingPathologyFoundationModels.

Keywords: Computational pathology; Few-shot learning; Fine-tuning; Foundation model.