4 papers
Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learning
Nanxi Yu, Kang Li, Ye Du +3
Domain incremental learning is essential for adapting ophthalmic deep learning models to sequential clinical domains while preserving diagnostic expertise. Existing domain incremen…
AD-CARE: A Guideline-grounded, Modality-agnostic LLM Agent for Real-world Alzheimer's Disease Diagnosis with Multi-cohort Assessment, Fairness Analysis, and Reader Study
Wenlong Hou, Sheng Bi, Guangqian Yang +16
Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, rea…
Medical Knowledge Intervention Prompt Tuning for Medical Image Classification
Ye Du, Nanxi Yu, Shujun Wang
Vision-language foundation models (VLMs) have shown great potential in feature transfer and generalization across a wide spectrum of medical-related downstream tasks. However, fine…
Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing
Ye Du, Chen Yang, Nanxi Yu +3
De novo peptide sequencing is a fundamental computational technique for ascertaining amino acid sequences of peptides directly from tandem mass spectrometry data, eliminating the n…