6 citations · 11 across the 10 of their papers we have counts for
13 papers
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…
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
Basar Demir, Yikang Liu, Xiao Chen +5
Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are…
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…
LSU-Net: Lightweight Automatic Organs Segmentation Network For Medical Images
Yujie Ding, Shenghua Teng, Zuoyong Li +1
UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make th…
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…
Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping
Yikang Liu, Lin Zhao, Eric Z. Chen +3
Dynamic coronary roadmapping is a technology that overlays the vessel maps (the "roadmap") extracted from an offline image sequence of X-ray angiography onto a live stream of X-ray…
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…
DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering
Zhongpai Gao, Benjamin Planche, Meng Zheng +3
Digitally reconstructed radiographs (DRRs) are simulated 2D X-ray images generated from 3D CT volumes, widely used in preoperative settings but limited in intraoperative applicatio…
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…
CAMEL2: Enhancing weakly supervised learning for histopathology images by incorporating the significance ratio
Gang Xu, Shuhao Wang, Lingyu Zhao +12
Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labour-i…