7 papers
Physical Layer Security for Movable Antenna-Aided Mixed-Field ISAC
Yun Xiao, Enhao Wang, Yunfei Chen +3
Movable antenna (MA) can enhance the physical layer security (PLS) performance of integrated sensing and communications (ISAC). However, most existing works assume either near-fiel…
CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding
Hanwen Zhang, Yao Liu, Die Dai +4
Fine-grained Vision-Language Pre-training (FVLP) demonstrates significant potential in 3D medical image understanding by aligning anatomy-level visual representations with correspo…
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
Bo Liu, Di Dai, Jingwei Liu +5
Granger Causal Discovery (GCD) is fundamental for analyzing temporal dependencies in complex systems. However, existing neural GCD methods predominantly rely on a "one-size-fits-al…
A Foundational Generative Model for Breast Ultrasound Image Analysis
Haojun Yu, Youcheng Li, Nan Zhang +20
Foundational models have emerged as powerful tools for addressing various tasks in clinical settings. However, their potential development to breast ultrasound analysis remains unt…
LarvSeg: Exploring Image Classification Data For Large Vocabulary Semantic Segmentation via Category-wise Attentive Classifier
Haojun Yu, Di Dai, Ziwei Zhao +3
Scaling up the vocabulary of semantic segmentation models is extremely challenging because annotating large-scale mask labels is labour-intensive and time-consuming. Recently, lang…
Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses
Haojun Yu, Youcheng Li, Nan Zhang +17
Data-driven deep learning models have shown great capabilities to assist radiologists in breast ultrasound (US) diagnoses. However, their effectiveness is limited by the long-tail…
PRED: Pre-training via Semantic Rendering on LiDAR Point Clouds
Hao Yang, Haiyang Wang, Di Dai +1
Pre-training is crucial in 3D-related fields such as autonomous driving where point cloud annotation is costly and challenging. Many recent studies on point cloud pre-training, how…