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By representing prototypes as orthonormal bases on the Stiefel manifold, this work makes prototype collapse infeasible by construction, leading to more interpretable and accurate image recognition.
By explicitly enforcing forward disease progression, D-ODR ensures that DR grading models learn biologically consistent feature representations, leading to more reliable clinical assessments.
