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Forget PEFT and KD, reprogramming distillation offers a surprisingly effective and robust way to adapt large medical foundation models to diverse downstream tasks.
Achieve state-of-the-art image-text matching by focusing on relevant fragments, using Optimal Transport to efficiently compute similarity between visual and textual elements.
By explicitly enforcing forward disease progression, D-ODR ensures that DR grading models learn biologically consistent feature representations, leading to more reliable clinical assessments.
