Encoding histopathological WSIs using GNN for scalable diagnostically relevant regions retrieval

The research on content-based histopathological image retrieval (CBHIR) has become popular in recent years. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pre-established database. To retrieve diagnostically relevant regions from a database that consists of histopathological whole slide images (WSIs) for query ROIs is challenging and yet significant for clinical applications. In this paper, we propose a novel CBHIR framework for regions retrieval from WSI-database based on hierarchical graph neural networks (GNNs). Compared to the present CBHIR framework, the structural information of WSI is preserved by the proposed model, which makes the retrieval framework more sensitive to regions that are similar in tissue distribution. Moreover, benefited from the hierarchical GNN structures, the proposed framework is scalable for both the size and shape variation of ROIs. It allows the pathologist defining the query region using free curves. Thirdly, the retrieval is achieved by binary codes and hashing methods, which makes it very efficient and thereby adequate for practical large-scale WSI-database. The proposed method is validated on a lung cancer dataset and compared to the state-of-the-art methods. The proposed method achieved precisions above 82.4% in the irregular region retrieval task, which are superior to the state-of-the-art methods. The average time of retrieval is 0.514 ms.