7 accepted papers
With the rapid growth of large language models (LLMs) and vision-language models (VLMs) in medicine, simply integrating clinical text and medical imaging does not guarantee reliable reasoning. Existing multimodal models often produce hallucinations or inconsistent chains of thought, limiting clinica…
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge, where the LLM
Retrieval-augmented generation (RAG) has become a fundamental paradigm for addressing the challenges faced by large language models in handling real-time information and domain-specific problems. Traditional RAG systems primarily rely on the in-context learning (ICL) capabilities of the large langua
Improving the performance of large language models (LLMs) in complex question-answering (QA) scenarios has always been a research focal point. Recent studies have attempted to enhance LLMs’ performance by combining step-wise planning with external retrieval. While effective for advanced models like…
Pulmonary nodule detection and segmentation are the necessary successively steps in lung cancer screening with low-dose computed tomography (CT) scans. However, the state-of-the-art models focus on solving tasks separately, thereby ignore the correlation between each task. Besides, most nodule detec…
Automatic pavement disease recognition is vital for pavement maintenance and management. In this paper, we present an end-to-end deep learning approach named Weakly Super-vised Patch Label Inference Network with Image Pyramid (WSPLIN-IP) for recognizing various types of pavement diseases that are no…
We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem, in which a collection of attribu…