2 citations · 4 across the 5 of their papers we have counts for
5 papers
Risk-Routed Implicit Boundary Refinement for Robust Ultrasound Image Segmentation
Jingguo Qu, Xinyang Han, Xiang Wang +8
Accurate boundary delineation in medical ultrasound (US) images remains challenging because of speckle noise, low contrast, acoustic shadowing, and acquisition variability. We prop…
Evidential Reasoning Advances Interpretable Real-World Disease Screening
Chenyu Lian, Hong-Yu Zhou, Jing Qin
Disease screening is critical for early detection and timely intervention in clinical practice. However, most current screening models for medical images suffer from limited interp…
Multiscale Switch for Semi-Supervised and Contrastive Learning in Medical Ultrasound Image Segmentation
Jingguo Qu, Xinyang Han, Yao Pu +8
Medical ultrasound image segmentation faces significant challenges due to limited labeled data and characteristic imaging artifacts including speckle noise and low-contrast boundar…
Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
Jingguo Qu, Xinyang Han, Jia Ai +10
Vision-Language Foundation Models (VLFMs) exhibit remarkable generalization, yet their direct application to medical ultrasound is severely hindered by a profound modality gap. The…
The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review
Jingguo Qu, Xinyang Han, Man-Lik Chui +8
Automatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrain…