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Updated: Sep 14, 2025
Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
Published on: March 20, 2018
Dual-track collaboration: Joint processing of heterogeneous positive and negative graph convolutional network for
Meiyan Liang1, Xikai Wang1, Bo Li2
1Shanxi Key Laboratory of Wireless Communication and Detection, School of Physics and Electronic Engineering, Shanxi University, Taiyuan 030006, China.
Background And Objective:
Graph-based methods are widely applied in whole-slide histopathology images (WSI) analysis since they can effectively capture spatial relationship between nodes. However, existing methods focus on promoting positive nodes to have similar representations while ignoring the expression of negative samples of each node, failing to fully utilize various diagnostic information for comprehensive analysis.
Methods:
In this paper, we propose a Dual Collaboration Heterogeneous Graph Convolutional Network (DCH-GCN) framework that considers both positive and negative samples implicit in whole-slide images (WSIs). Specifically, the framework consists of two complementary graphs: a positive edge homogeneous subgraph (PEH-subgraph), constructed using positive samples, and a negative edge heterogeneous subgraph (NEH-subgraph), built from negative samples. These two subgraphs collaboratively capture discriminative patch features within WSIs. The PEH-subgraph encourages spatially adjacent patches to learn similar feature representations, whereas the NEH-subgraph utilizes negative samples to enhance difference for patches exhibiting distinct morphology. In addition, we introduce a negative sample selection principle based on k-DPP and a two-stage instance clustering process to ensure the diversity and rationality of selected negative samples.
Results:
Our method was evaluated on three public datasets, achieving an ACC of 0.937, AUC of 0.943, and F1 score of 0.952 on CAMELYON16 for cancer identification; an ACC of 0.923, AUC of 0.965, and F1 score of 0.926 on TCGA-NSCLC for subtype classification; and an ACC of 0.453, AUC of 0.648, and F1 score of 0.445 on TCGA-COAD for cancer staging.
Conclusions:
Selecting appropriate negative and positive samples for each patch to construct DCH-GCN can more comprehensively represent the topological information of WSI images and improve the overall prediction performance.
