2 citations · 2 across the 12 of their papers we have counts for
14 papers
Incentivizing Tool-Use Reinforcement Learning for Post-Arrival Emergency Care Prediction
Pengfei Hu, Chenwei Wu, Qiao Jin +4
Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for emergency department (ED) prediction. However, most…
DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening
Yung Wei Shueh, Zhi-Jie Chen, Chia-Hsuan Hsu +9
Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and citation errors. We present DIA…
MedCache: Efficient and Temporally Valid Memory for Longitudinal Clinical Agents
Hei Ting, Chan, Chenwei Wu +5
Longitudinal clinical agents must maintain an evolving patient state from evidence distributed across visits, time points, and specialties. However, how agent memory should be desi…
A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings
Hei Ting, Chan, Chenwei Wu +8
Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural settings where bandwidth is scarc…
TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification
Yu Chang, Anzhe Cheng, Chenwei Wu +7
The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction. Recent multimodal models have adv…
Evaluating and Understanding Model Editing for Medical Vision Language Models
Guli Zhu, Chenwei Wu, Liyue Shen
Model editing promises a fast, targeted way to correct post-deployment mistakes in medical vision-language models (VLMs) without costly retraining. However, existing multimodal mod…
Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning
Diana Guadalupe Gomez, Chenwei Wu, Zhiyi Wang +2
Epidemic forecasting models typically rely on surveillance data reported over administrative regions, treating them as atomic units, thereby obscuring sub-regional spatial structur…
MedAction: Towards Active Multi-turn Clinical Diagnostic LLMs
Hsin-Ling Hsu, Zizheng Wang, Donghua Zhang +9
Most existing LLM diagnoses are evaluated on static, single-turn settings where complete patient information is provided upfront, an oversimplification of real clinical practice. W…
REMIND: Rethinking Medical High-Modality Learning under Missingness--A Long-Tailed Distribution Perspective
Chenwei Wu, Zitao Shuai, Liyue Shen
Medical multi-modal learning is critical for integrating information from a large set of diverse modalities. However, when leveraging a high number of modalities in real clinical a…
SeqBench: Benchmarking Sequential Narrative Generation in Text-to-Video Models
Zhengxu Tang, Zizheng Wang, Luning Wang +8
Text-to-video (T2V) generation models have made significant progress in creating visually appealing videos. However, they struggle with generating coherent sequential narratives th…
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation
Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang +12
Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We id…
Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing
Zitao Shuai, Chenwei Wu, Zhengxu Tang +2
Diffusion Transformers (DiTs) have recently achieved remarkable success in text-guided image generation. In image editing, DiTs project text and image inputs to a joint latent spac…
Latent Space Disentanglement in Diffusion Transformers Enables Zero-shot Fine-grained Semantic Editing
Zitao Shuai, Chenwei Wu, Zhengxu Tang +2
Diffusion Transformers (DiTs) have achieved remarkable success in diverse and high-quality text-to-image(T2I) generation. However, how text and image latents individually and joint…
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity
Zitao Shuai, Chenwei Wu, Zhengxu Tang +1
Vision-language pre-training (VLP) has emerged as an effective scheme for multimodal representation learning, but its reliance on large-scale multimodal data poses significant chal…