Graph-enhanced deep learning for diabetic retinopathy diagnosis: A quality-aware and uncertainty-driven approach
Fig 2
Model architecture: The dataset undergoes basic preprocessing (e.g., resizing, transformations, rotations) to prepare the data.
The FE function processes each sample
to generate a feature vector (FV)
. This FV is then refined to a vector in
using Global Average Pooling (GAP). A graph is constructed with nodes corresponding to FVs, and the edge distance is computed based on spatial distance
and semantic distance
.