  Histopathological whole slide images (WSIs) classification has become a
foundation task in medical microscopic imaging processing. Prevailing
approaches involve learning WSIs as instance-bag representations, emphasizing
significant instances but struggling to capture the interactions between
instances. Additionally, conventional graph representation methods utilize
explicit spatial positions to construct topological structures but restrict the
flexible interaction capabilities between instances at arbitrary locations,
particularly when spatially distant. In response, we propose a novel dynamic
graph representation algorithm that conceptualizes WSIs as a form of the
knowledge graph structure. Specifically, we dynamically construct neighbors and
directed edge embeddings based on the head and tail relationships between
instances. Then, we devise a knowledge-aware attention mechanism that can
update the head node features by learning the joint attention score of each
neighbor and edge. Finally, we obtain a graph-level embedding through the
global pooling process of the updated head, serving as an implicit
representation for the WSI classification. Our end-to-end graph representation
learning approach has outperformed the state-of-the-art WSI analysis methods on
three TCGA benchmark datasets and in-house test sets. Our code is available at
https://github.com/WonderLandxD/WiKG.
