Relation-Aware Graph Attention Network for Nuclei Classification

Nuclei classification plays a pivotal role in pathological research. Recent advances in graph neural networks (GNNs) have shown great promise in modeling cell-cell interactions. However, many existing methods overlook tissue context, which is crucial for accurate nuclei identification, as nuclei exhibit distinct patterns within specific tissue structures. To address this limitation, we propose a novel Relation-Aware Graph AT-tention network (RAGAT) that effectively leverages nucleus-related features for precise classification. RAGAT constructs a cell graph based on spatial proximity and visual feature similarity, while also introducing a tissue-aware graph by sampling regions around each nucleus to capture the tissue microenvironment and depict local cellular contexts. Furthermore, RAGAT employs a hybrid graph attention module to integrate cell-cell and tissue-cell interactions, enabling a comprehensive understanding of the nuclear context. Experimental results on three benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches, offering valuable insight into the analysis of nuclear microenvironments. Our code is available at https://github.com/lingboboo/RAGAT.