Methods and apparatus for analyzing pathology patterns of whole-slide images based on graph deep learning

An apparatus for analyzing pathology patterns of whole-slide images (WSI) using graph deep learning, the apparatus comprising:

• a WSI compression unit, comprising a processor and memory, configured to segment the WSI into a plurality of small patches, extract patch-level features using a pretrained model, and compress the patches into a superpatch graph by grouping spatially adjacent and feature-similar patches into superpatches, each represented as a node;
• a graph analysis unit, configured to embed node features and spatial context features of the superpatch graph using a graph neural network (GNN), wherein the context features include distance and angle between superpatches;
• a contribution analysis unit, configured to compute contribution scores for each node and edge using an integrated gradients method or another attribution method for interpretability;
• a graph grouping unit, configured to classify and group superpatches based on their contribution scores to identify connected subgraphs representing environmental graph biomarkers;
• a biomarker acquisition unit, configured to extract environmental graph biomarkers by analyzing spatial topology of high-contribution subgraphs;
• and
• a diagnostic information extraction unit, configured to generate and provide a diagnostic score, based on the identified graph biomarkers and their spatial distribution in the WSI.