The application provides a
granulocyte-guided double-space semantic calibration clustering method for
diabetic retinopathy, comprising the following steps: step 1, designing an independent
encoder to extract view-specific features; step 2, performing view quality evaluation on the view-specific embedding features output by each independent
encoder, and calculating the confidence weight of each view; step 3, realizing double-space collaborative semantic calibration in the feature space and the clustering space; step 4, dividing the
lesion semantic unit of
diabetic retinopathy through
granulocyte pre-modeling, and screening an initial clustering center from high-confidence granulocytes; and step 5, dynamically adjusting the exploration, utilization strategy and clustering center update step length based on the
granulocyte confidence, and combining the semantic calibration
signal to realize adaptive clustering optimization. The method helps to improve the accuracy and consistency of the double-view image staging clustering of
diabetic retinopathy, and significantly reduces the early
lesion missed diagnosis rate and the medium-term staging
confusion rate.