16 citations · 18 across the 7 of their papers we have counts for
7 papers
Task-Relevant Feature-Dynamics Fidelity Enables Zero-Shot Sim-to-Real Transfer for Robotic Ultrasound Scanning
Yizhao Qian, Jiayuan Luo, Wanyi Zhu +4
Robotic ultrasound policies operating directly on B-mode images require extensive interaction data, whereas real-robot data collection is costly and safety-constrained. Simulation…
UniMedSeg: Unified In-Context Learning for Multi-Paradigm 2D/3D Medical Image Segmentation
Yunzhou Li, Jiesi Hu, Yanwu Yang +5
Medical image segmentation foundation models are expected to generalize across diverse clinical scenarios, yet existing universal methods remain fragmented by prompt paradigms and…
VDE Bench: Evaluating The Capability of Image Editing Models to Modify Visual Documents
Hongzhu Yi, Yujia Yang, Yuanxiang Wang +18
Image editing models can modify visual content through natural language, but editing dense documents requires precise local changes amid tightly coupled text, tables, and layout. W…
Breaking the Latency Barrier: Synergistic Perception and Control for High-Frequency 3D Ultrasound Servoing
Yizhao Qian, Yujie Zhu, Jiayuan Luo +4
Real-time tracking of dynamic targets amidst large-scale, high-frequency disturbances remains a critical unsolved challenge in Robotic Ultrasound Systems (RUSS), primarily due to t…
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Wei Dai, Rui Liu, Zixuan Wu +5
Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment. Identifying mild syndrom…
Learning to Adapt Foundation Model DINOv2 for Capsule Endoscopy Diagnosis
Bowen Zhang, Ying Chen, Long Bai +5
Foundation models have become prominent in computer vision, achieving notable success in various tasks. However, their effectiveness largely depends on pre-training with extensive…
Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation
Zhen Zhao, Zicheng Wang, Longyue Wang +3
Semi-supervised medical image segmentation studies have shown promise in training models with limited labeled data. However, current dominant teacher-student based approaches can s…