In computation pathology, the pyramid structure of gigapixel Whole Slide Images (WSIs) has recently been studied for capturing various information from individual cell interactions to tissue microenvironments. This hierarchical structure is believed to be beneficial for cancer diagnosis and prognosis tasks. However, most previous hierarchical WSI analysis works (1) only characterize local or global correlations within the WSI pyramids and (2) use only unidirectional interaction between different resolutions, leading to an incomplete picture of WSI pyramids. To this end, this paper presents a novel Hierarchical Interaction Graph-Transformer (i.e., HIGT) for WSI analysis. With Graph Neural Network and Transformer as the building commons, HIGT can learn both short-range local information and long-range global representation of the WSI pyramids. Considering that the information from different resolutions is complementary and can benefit each other during the learning process, we further design a novel Bidirectional Interaction block to establish communication between different levels within the WSI pyramids. Finally, we aggregate both coarse-grained and fine-grained features learned from different levels together for slide-level prediction. We evaluate our methods on two public WSI datasets from TCGA projects, i.e., kidney carcinoma (KICA) and esophageal carcinoma (ESCA). Experimental results show that our HIGT outperforms both hierarchical and non-hierarchical state-of-the-art methods on both tumor subtyping and staging tasks.

翻译：在计算病理学中，巨像素全切片图像（WSI）的金字塔结构近期被用于捕捉从单个细胞相互作用到组织微环境的多种信息。这种层次结构被认为有益于癌症诊断和预后任务。然而，大多数以往的层次化WSI分析方法（1）仅表征WSI金字塔内的局部或全局相关性，（2）仅使用不同分辨率间的单向交互，导致对WSI金字塔的理解不完整。为此，本文提出了一种新颖的层次交互图-Transformer（即HIGT）用于WSI分析。以图神经网络和Transformer作为基本组件，HIGT能够同时学习WSI金字塔的短程局部信息和长程全局表征。考虑到不同分辨率的信息具有互补性，且在学习过程中能相互促进，我们进一步设计了一种新颖的双向交互模块，以建立WSI金字塔不同层级间的通信。最后，我们聚合从不同层级学到的粗粒度和细粒度特征，用于切片级预测。我们在TCGA项目的两个公开WSI数据集（即肾癌（KICA）和食管癌（ESCA））上评估了我们的方法。实验结果表明，我们的HIGT在肿瘤亚型分类和分期任务中均优于现有的层次化和非层次化最优方法。