Integrating Foundation Model Features into Graph Neural Network and Fusing Predictions with Standard Fine-Tuned Models for Histology Image Classification.

ABSTRACT: Histopathological image classification using computational methods such as fine-tuned convolutional neural networks (CNNs) has gained significant attention in recent years. Graph neural networks (GNNs) have also emerged as strong alternatives, often employing CNNs or vision transformers (ViTs) as node feature extractors. However, as these models are usually pre-trained on small-scale natural image datasets, their performance in histopathology tasks can be limited. The introduction of foundation models trained on large-scale histopathological data now enables more effective feature extraction for GNNs. In this work, we integrate recently developed foundation models as feature extractors within a lightweight GNN and compare their performance with standard fine-tuned CNN and ViT models. Furth

SUBMITTER: Saeidi N 

PROVIDER: S-EPMC12729508 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature