most citedAdapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Risk-Routed Implicit Boundary Refinement for Robust Ultrasound Image Segmentation

Jingguo Qu, Xinyang Han, Xiang Wang +8

Medical ultrasound (US) image segmentation faces significant challenges due to speckle noise, low-contrast boundaries, acoustic shadowing, and acquisition variation across operator…

cs.CV2026

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…

cs.CV20261 cited

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…

cs.CV20261 cited

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…

eess.IV2025

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…