The invention relates to a
biomedical image feature fusion method based on a multi-scale heterogeneous
hypergraph. The method comprises the following steps: S1, constructing a
cell-level
hypergraph model; s2, constructing a region-level
hypergraph model; s3, constructing a sample-level hypergraph model; s4, transmission and fusion of cross-level hypergraph features: through a level graph neural network HGCN and a dynamic attention mechanism, transmission of
cell-level hypergraph features-region-level hypergraph features-sample-level hypergraph features is carried out, global alignment is carried out, and global hypergraph features are obtained; and S5, constructing a histomorphological classification model by using the global hypergraph features, and outputting sample feature representation. According to the method, through multi-scale heterogeneous hypergraph modeling and cross-level
dynamic feature fusion, the problems of single-scale characterization limitation and heterogeneous data high-order interaction
bottleneck are solved, the cross-scale characterization capability of the
biological tissue microenvironment is remarkably improved, the adaptability, generalization performance and analysis precision of a classification model to complex data scenes are enhanced, and the method is suitable for being popularized and applied. And a general framework is provided for complex biomedical analysis tasks.