34 citations · 72 across the 29 of their papers we have counts for
14 papers · 1 filter
SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models
Zefan Yang, Zheyuan Zhang, Boris Maihe +7
Robotic ultrasound can standardize image acquisition and reduce operator workload and variability, but policy learning remains limited by the lack of realistic, controllable simula…
Learning Where to Look: Anatomical Grounding and Guided Attention for Cardiac MRI Vision-Language Models
Bangwei Guo, Xiao Chen, Boris Mailhe +8
Cardiac magnetic resonance imaging (CMR) enables assessment of cardiac anatomy, ventricular function, and myocardial tissue characteristics. Clinicians interpret these images by id…
RAU: Reference-based Anatomical Understanding with Vision Language Models
Yiwei Li, Yikang Liu, Jiaqi Guo +7
Anatomical understanding, which is the ability to identify, localize, or segment anatomical structures, is critical in medical image analysis; however, its progress is constrained…
Leveraging Diffusion Model and Image Foundation Model for Improved Correspondence Matching in Coronary Angiography
Lin Zhao, Xin Yu, Yikang Liu +4
Accurate correspondence matching in coronary angiography images is crucial for reconstructing 3D coronary artery structures, which is essential for precise diagnosis and treatment…
Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation
Xiaoran Zhang, Eric Z. Chen, Lin Zhao +6
We propose a novel approach that adapts hierarchical vision foundation models for real-time ultrasound image segmentation. Existing ultrasound segmentation methods often struggle w…
Label-Efficient Data Augmentation with Video Diffusion Models for Guidewire Segmentation in Cardiac Fluoroscopy
Shaoyan Pan, Yikang Liu, Lin Zhao +4
The accurate segmentation of guidewires in interventional cardiac fluoroscopy videos is crucial for computer-aided navigation tasks. Although deep learning methods have demonstrate…
Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models
Lin Zhao, Xiao Chen, Eric Z. Chen +3
Medical image segmentation is crucial for clinical decision-making, but the scarcity of annotated data presents significant challenges. Few-shot segmentation (FSS) methods show pro…
Federated Data Model
Xiao Chen, Shunan Zhang, Eric Z. Chen +4
In artificial intelligence (AI), especially deep learning, data diversity and volume play a pivotal role in model development. However, training a robust deep learning model often…
Robust Landmark-based Stent Tracking in X-ray Fluoroscopy
Luojie Huang, Yikang Liu, Li Chen +3
In clinical procedures of angioplasty (i.e., open clogged coronary arteries), devices such as balloons and stents need to be placed and expanded in arteries under the guidance of X…
Deep Statistic Shape Model for Myocardium Segmentation
Xiaoling Hu, Xiao Chen, Yikang Liu +3
Accurate segmentation and motion estimation of myocardium have always been important in clinic field, which essentially contribute to the downstream diagnosis. However, existing me…
Multi-scale Neural ODEs for 3D Medical Image Registration
Junshen Xu, Eric Z. Chen, Xiao Chen +2
Image registration plays an important role in medical image analysis. Conventional optimization based methods provide an accurate estimation due to the iterative process at the cos…
FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation
Hanchao Yu, Shanhui Sun, Haichao Yu +4
Motion estimation of cardiac MRI videos is crucial for the evaluation of human heart anatomy and function. Recent researches show promising results with deep learning-based methods…
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning
Shanhui Sun, Jing Hu, Mingqing Yao +4
The crucial components of a conventional image registration method are the choice of the right feature representations and similarity measures. These two components, although elabo…
DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition
Benjamin Planche, Ziyan Wu, Kai Ma +7
Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data. Collecting such datasets is however a tedious, often impos…