PLM-SynNet: A Pathology Large Model Synergy Network Based on Multi-instance Learning for Whole Slide Imaging Classification.

Whole slide imaging (WSI) provides rich tissue information at a gigapixel resolution, posing significant challenges for the development of pathology analysis algorithms. Mainstream approaches effectively analyze WSI but treat the pretrained feature extractor and the task-specific network as independent modules, thereby restricting downstream task accuracy due to the limitations of the pretrained model. Inspired by the knowledge complementarity mechanism in multi-agent collaboration, we propose PLM-SynNet, a pathology large model synergy network that integrates the strengths of multiple pathology large models (PLMs) by establishing a flexible collaborative structure and achieving information gain. Specifically, the PLM Synergy Block (PLM-SB) is designed based on Mixture of Experts (MoE), which flexibly generates and utilizes supplementary features by employing a feature generator as an expert in MoE and merging these outputs via pixel-wise summation for effective collaboration. Subsequently, a Synergy Reinforcement Loss (SRLoss) is defined to enhance the information gain of multiple PLMs by enforcing stricter constraints on both queried and generated features. Experiments on a private PCA-EPE (Extraprostatic Extension of Prostate Cancer) dataset and two public datasets demonstrate the effectiveness of the proposed method, yielding gains of 13.27% in F1-score, 6.30% in accuracy, and 15.16% in MCC on PCA-EPE. It further improves TCGA-CRC accuracy by 1.71% and enhances BRIGHT accuracy and AUC by 2.67% and 4.00%, respectively. The code repository is available at https://github.com/mathfyy/PLM-SynNet.