1 citations · 2 across the 9 of their papers we have counts for
10 papers
Deep Reprogramming Distillation for Medical Foundation Models
Siyuan Du, Yuhang Zhou, Haolin Li +5
Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation models for specific medical scena…
Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach
Haolin Li, Shuyang Jiang, Ruipeng Zhang +3
While large language models hold promise for complex medical applications, their development is hindered by the scarcity of high-quality reasoning data. To address this issue, exis…
Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model
Siyuan Du, Siyi Li, Shuwei Bai +10
Parkinson's disease (PD) affects over ten million people worldwide. Although temporal interference (TI) and deep brain stimulation (DBS) are promising therapies, inter-individual v…
RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosis
Haolin Li, Tianjie Dai, Zhe Chen +4
Clinical diagnosis is a highly specialized discipline requiring both domain expertise and strict adherence to rigorous guidelines. While current AI-driven medical research predomin…
HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks
Zhe Chen, Yusheng Liao, Zhiyuan Zhu +4
Medical large vision-language Models (Med-LVLMs) have shown promise in clinical applications but suffer from factual inaccuracies and unreliable outputs, posing risks in real-world…
LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models
Haolin Li, Yuhang Zhou, Ziheng Zhao +5
The widespread adoption of large-scale pre-training techniques has significantly advanced the development of medical foundation models, enabling them to serve as versatile tools ac…
Reprogramming Distillation for Medical Foundation Models
Yuhang Zhou, Siyuan Du, Haolin Li +3
Medical foundation models pre-trained on large-scale datasets have demonstrated powerful versatile capabilities for various tasks. However, due to the gap between pre-training task…
Exploring Training on Heterogeneous Data with Mixture of Low-rank Adapters
Yuhang Zhou, Zihua Zhao, Haolin Li +4
Training a unified model to take multiple targets into account is a trend towards artificial general intelligence. However, how to efficiently mitigate the training conflicts among…
Low-Rank Knowledge Decomposition for Medical Foundation Models
Yuhang Zhou, Haolin Li, Siyuan Du +3
The popularity of large-scale pre-training has promoted the development of medical foundation models. However, some studies have shown that although foundation models exhibit stron…
RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detection
Haolin Li, Shuyang Jiang, Lifeng Zhang +3
Fraud detection remains a challenging task due to the complex and deceptive nature of fraudulent activities. Current approaches primarily concentrate on learning only one perspecti…