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Towards Proactive Personalization through Profile Customization for Individual Users in Dialogues
Xiaotian Zhang, Yuan Wang, Ruizhe Chen +3
The deployment of Large Language Models (LLMs) in interactive systems necessitates a deep alignment with the nuanced and dynamic preferences of individual users. Current alignment…
Beyond N-grams: A Hierarchical Reward Learning Framework for Clinically-Aware Medical Report Generation
Yuan Wang, Shujian Gao, Jiaxiang Liu +6
Automatic medical report generation can greatly reduce the workload of doctors, but it is often unreliable for real-world deployment. Current methods can write formally fluent sent…
Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding
Songtao Jiang, Yuan Wang, Sibo Song +22
Real-world clinical decision-making requires integrating heterogeneous data, including medical text, 2D images, 3D volumes, and videos, while existing AI systems fail to unify all…
Modest-Align: Data-Efficient Alignment for Vision-Language Models
Jiaxiang Liu, Yuan Wang, Jiawei Du +3
Cross-modal alignment aims to map heterogeneous modalities into a shared latent space, as exemplified by models like CLIP, which benefit from large-scale image-text pretraining for…
Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset
Ziye Deng, Ruihan He, Jiaxiang Liu +5
Medical image grounding aims to align natural language phrases with specific regions in medical images, serving as a foundational task for intelligent diagnosis, visual question an…
V2T-CoT: From Vision to Text Chain-of-Thought for Medical Reasoning and Diagnosis
Yuan Wang, Jiaxiang Liu, Shujian Gao +5
Recent advances in multimodal techniques have led to significant progress in Medical Visual Question Answering (Med-VQA). However, most existing models focus on global image featur…
Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning
Xiaotian Zhang, Yuan Wang, Zhaopeng Feng +6
Medical Question-Answering (QA) encompasses a broad spectrum of tasks, including multiple choice questions (MCQ), open-ended text generation, and complex computational reasoning. D…
CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making
Songtao Jiang, Yuan Wang, Ruizhe Chen +8
In medical visual question answering (Med-VQA), achieving accurate responses relies on three critical steps: precise perception of medical imaging data, logical reasoning grounded…
OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding
Songtao Jiang, Yuan Wang, Sibo Song +6
The practical deployment of medical vision-language models (Med-VLMs) necessitates seamless integration of textual data with diverse visual modalities, including 2D/3D images and v…