面向全切片图像分类中特征稀缺问题的双层超图生成方法
Dual-Level Hypergraph Generation for Addressing Feature Scarcity in Whole-Slide Image Classification
针对全切片图像中少数类特征稀缺问题，提出超图层级的提示引导分层超图变分自编码器生成拓扑一致的超图表示，以及超节点层级的锚框扩散 mixup 增强高注意力锚节点特征，可迁移到其他存在类内/类间稀缺的病理或细粒度分类任务。
原文依据 we develop a prompt-guided hierarchical hypergraph variational autoencoder (HGVAE)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论病理图像中的淋巴结转移诊断是一项极具挑战性的四分类任务，包括宏转移、微转移、孤立肿瘤细胞（ITC）和阴性病变。与传统分类设定不同，这一四分类场景同时面临少数类信息的类间稀缺与切片内稀缺问题。现有基于 CNN 或 GNN 的方法主要侧重于节点级特征学习，难以捕捉细胞间的高阶特征交互与拓扑依赖关系，同时也忽视了类别稀缺所导致的表征不足问题。为应对这些挑战，我们提出一种双层生成框架，将类别提示先验与高阶结构建模相结合，以增强少数类的表征能力。在超图层级，我们设计了一种提示引导的分层超图变分自编码器（HGVAE），能够为少数类生成多样且拓扑一致的超图表示。在超节点层级，我们引入锚框扩散 mixup 策略，以丰富高注意力正锚节点的少数类节点特征。在四分类 NIMM 数据集以及 TCGA 数据集上的大量实验表明，所提框架有效缓解了特征稀缺问题，并显著提升了少数类的分类性能。
英文原文摘要 1449 字符
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 (HGVAE) capable of generating diverse and topologically consistent hypergraph representations for minority classes.At the hypernode level, we introduce an anchor-diffusion 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.