HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis
This work addresses a specific bottleneck in computational pathology for cancer diagnosis by improving WSI analysis through a novel hierarchical method, though it appears incremental as it builds on existing Graph Neural Network and Transformer components.
The paper tackles the problem of incomplete hierarchical analysis in Whole Slide Images (WSIs) for cancer diagnosis and prognosis by proposing HIGT, a Hierarchical Interaction Graph-Transformer that learns both local and global representations and uses bidirectional interactions between resolution levels, resulting in outperforming state-of-the-art methods on tumor subtyping and staging tasks across two TCGA datasets.
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.