Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?

• URL: http://arxiv.org/abs/2502.20823v1
• Date: Fri, 28 Feb 2025 08:10:30 GMT
• Title: Can We Simplify Slide-level Fine-tuning of Pathology Foundation Models?
• Authors: Jiawen Li, Jiali Hu, Qiehe Sun, Renao Yan, Minxi Ouyang, Tian Guan, Anjia Han, Chao He, Yonghong He,
• Abstract summary: We present a key experimental finding: a simple nonlinear mapping strategy combining mean pooling and a multilayer perceptron, called SiMLP, can effectively adapt foundation models to slide-level tasks without complex MIL-based learning.<n>Our findings challenge the conventional MIL-based fine-tuning paradigm, demonstrating that a task-agnostic representation strategy alone can effectively adapt foundation models to WSI analysis.
• Score: 5.888444803253168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of foundation models in computational pathology has transformed histopathological image analysis, with whole slide imaging (WSI) diagnosis being a core application. Traditionally, weakly supervised fine-tuning via multiple instance learning (MIL) has been the primary method for adapting foundation models to WSIs. However, in this work we present a key experimental finding: a simple nonlinear mapping strategy combining mean pooling and a multilayer perceptron, called SiMLP, can effectively adapt patch-level foundation models to slide-level tasks without complex MIL-based learning. Through extensive experiments across diverse downstream tasks, we demonstrate the superior performance of SiMLP with state-of-the-art methods. For instance, on a large-scale pan-cancer classification task, SiMLP surpasses popular MIL-based methods by 3.52%. Furthermore, SiMLP shows strong learning ability in few-shot classification and remaining highly competitive with slide-level foundation models pretrained on tens of thousands of slides. Finally, SiMLP exhibits remarkable robustness and transferability in lung cancer subtyping. Overall, our findings challenge the conventional MIL-based fine-tuning paradigm, demonstrating that a task-agnostic representation strategy alone can effectively adapt foundation models to WSI analysis. These insights offer a unique and meaningful perspective for future research in digital pathology, paving the way for more efficient and broadly applicable methodologies.

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