The heterogeneity of breast cancer presents considerable challenges for its early detection, prognosis, and treatment selection. Convolutional neural networks often neglect the spatial relationships within histopathological images, which can limit their accuracy. Graph neural networks (GNNs) offer a promising solution by coding the spatial relationships within images. Prior studies have investigated the modeling of histopathological images as cell and tissue graphs, but they have not fully tapped into the potential of extracting interrelationships between these biological entities. In this paper, we present a novel approach using a heterogeneous GNN that captures the spatial and hierarchical relations between cell and tissue graphs to enhance the extraction of useful information from histopathological images. We also compare the performance of a cross-attention-based network and a transformer architecture for modeling the intricate relationships within tissue and cell graphs. Our model demonstrates superior efficiency in terms of parameter count and achieves higher accuracy compared to the transformer-based state-of-the-art approach on three publicly available breast cancer datasets -- BRIGHT, BreakHis, and BACH.

翻译：乳腺癌的异质性为其早期检测、预后判断及治疗方案选择带来了重大挑战。卷积神经网络常忽略组织病理图像中的空间关系，从而限制了诊断精度。图神经网络通过编码图像中的空间关系提供了有效解决方案。现有研究虽已探索将组织病理图像建模为细胞图与组织图，但尚未充分挖掘这些生物学实体间交互相连关系的潜在价值。本文提出一种基于异构图神经网络的新方法，通过捕获细胞图与组织图之间的空间层级关系，增强从组织病理图像中提取有效信息的能力。同时，我们比较了基于交叉注意力机制的网络与Transformer架构在建模组织-细胞图复杂关系中的性能表现。在BRIGHT、BreakHis和BACH三个公开乳腺癌数据集上，本模型在参数效率与诊断准确率方面均优于基于Transformer的现有最优方法。