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.

本文提出了一种用于 WSI 分析的新型 Hierarchical Interaction Graph-Transformer（HIGT），通过学习 WSI 金字塔的局部和全局特征，并建立不同层次之间的双向交互，实现了肿瘤亚型和分期任务上的优于现有方法的性能。