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College of Computer Science and Technology, Zhejiang University, Hangzhou, China
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DeepBD outperforms traditional variant prioritization tools by integrating LLMs and specialized evidence modules, achieving a Recall@10 of 92.9% on a large cohort of genetic cases.
Static labels in medical AI are giving way to dynamic models that can simulate disease evolution and guide personalized interventions.
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.
