260 citations · 333 across the 15 of their papers we have counts for
7 papers · 1 filter
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
Cheng Chen, Juzheng Miao, Dufan Wu +10
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation ta…
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
Bin Yin, Junjie Xie, Yu Qin +4
The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems. However, the conventional approach of incorporating various types of h…
StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation
Xiang Li, Shunpan Liang, Yulei Hou +1
With the growing imbalance between limited medical resources and escalating demands, AI-based clinical tasks have become paramount. As a sub-domain, medication recommendation aims…
Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Yuting Zhang, Yiqing Wu, Ran Le +7
Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from trad…
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
Di Jin, Luzhi Wang, Yizhen Zheng +5
Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations n…
BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
Kai Zhang, Rong Zhou, Eashan Adhikarla +20
Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to ut…
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking
Xiang Li, Tiandi Ye, Caihua Shan +2
Generative graph self-supervised learning (SSL) aims to learn node representations by reconstructing the input graph data. However, most existing methods focus on unsupervised lear…