Distilling heterogeneous knowledge with aligned biological entities for histological image classification

In the task of classifying histological images, prior works widely leverage Graph neural network (GNN) to aggregate histological knowledge from multi-level biological entities (e.g., cell and tissue). However, current GNN-based methods suffer from either inadequate entity representation or intolerable computation burden. To the end, we propose a heterogeneous knowledge distillation (HKD) model to capture and amalgamate the spatial-hierarchical feature of multi-level biological entities. We first design multiple message-passing GNNs with different hidden layers as the teachers for extracting adjacent regions of cells, and leverage a transformer-based GNN as the student to model the global interaction of tissues. Such multi-teacher student architecture enables our HKD to simultaneously obtain topological knowledge at different scales from heterogeneous biological entities. We further propose a biological affiliation recognition module to adaptively align the cell knowledge learned from multi-teacher models with cell-corresponding tissue in the student model, encouraging the student model to attentively amalgamate the semantics of multi-level biological entities for highly accurate classification. Extensive experiments show that our method outperforms the state-of-the-art on three public datasets of histological image classification.