Fusion of Heterogeneous Pathology Foundation Models for Whole Slide Image Analysis

2025-10-31

AI Summary

In pathological whole-slide image (WSI) analysis, heterogeneous foundation models (FMs) suffer from feature inconsistency and downstream performance instability due to disparities in training data and architectural design. To address this, we propose FuseCPath—a novel framework that (1) selects discriminative image patches via multi-view clustering, (2) models local patch representations using cluster-level online re-embedding, and (3) exploits cross-model global representation correlations through collaborative distillation. To our knowledge, FuseCPath is the first method to systematically fuse heterogeneous FM features at both patch-level and slide-level. Evaluated on TCGA lung, bladder, and colorectal cancer datasets, it achieves significant improvements across multiple tasks—including classification and survival prediction—while demonstrating superior generalizability and stability over state-of-the-art approaches.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Computer Vision: Other Foundations of Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Whole slide image (WSI) analysis has emerged as an increasingly essential technique in computational pathology. Recent advances in the pathological foundation models (FMs) have demonstrated significant advantages in deriving meaningful patch-level or slide-level feature representations from WSIs. However, current pathological FMs have exhibited substantial heterogeneity caused by diverse private training datasets and different network architectures. This heterogeneity introduces performance variability when we utilize the extracted features from different FMs in the downstream tasks. To fully explore the advantage of multiple FMs effectively, in this work, we propose a novel framework for the fusion of heterogeneous pathological FMs, called FuseCPath, yielding a model with a superior ensemble performance. The main contributions of our framework can be summarized as follows: (i) To guarantee the representativeness of the training patches, we propose a multi-view clustering-based method to filter out the discriminative patches via multiple FMs' embeddings. (ii) To effectively fuse the heterogeneous patch-level FMs, we devise a cluster-level re-embedding strategy to online capture patch-level local features. (iii) To effectively fuse the heterogeneous slide-level FMs, we devise a collaborative distillation strategy to explore the connections between slide-level FMs. Extensive experiments conducted on lung cancer, bladder cancer, and colorectal cancer datasets from The Cancer Genome Atlas (TCGA) have demonstrated that the proposed FuseCPath achieves state-of-the-art performance across multiple tasks on these public datasets.

Problem

• Fusing heterogeneous pathology foundation models for WSI analysis
• Addressing performance variability from diverse training datasets and architectures
• Enhancing ensemble performance across multiple cancer diagnosis tasks

Innovation

• Fuses heterogeneous pathology foundation models
• Uses multi-view clustering for discriminative patches
• Applies collaborative distillation for slide-level fusion