Dual-Level Hypergraph Generation for Addressing Feature Scarcity in Whole-Slide Image Classification

Lymph node metastasis diagnosis in pathological images is a highly challenging four-class classification task, comprising macrometastasis, micrometastasis, isolated tumor cells (ITC), and negative lesions. Unlike conventional classification settings, this four-class scenario simultaneously suffers from inter-class and intra-slide scarcity of minority information. Existing approaches based on CNNs or GNNs primarily emphasize node-level feature learning, making it difficult to capture high-order feature interactions and topological dependencies among cells, while also overlooking the representational insufficiency induced by class scarcity. To address these challenges, we propose a dual-level generative framework that integrates class-prompt priors with high-order structural modeling to enhance the representation capacity of minority classes. At the hypergraph level, we develop a prompt-guided hierarchical hypergraph variational autoencoder capable of generating diverse and topologically consistent hypergraph representations for minority classes. At the hypernode level, we introduce an anchordiffusion mixup strategy to enrich the minority node features of high-attention positive anchor nodes. Extensive experiments on the four-class NIMM dataset, as well as TCGA datasets, demonstrate that the proposed framework effectively alleviates feature scarcity and significantly boosts the classification performance of minority classes. The code is