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.LG2026

MedExpMem: Adapting Experience Memory for Differential Diagnosis

Qianhan Feng, Zhongzhen Huang, Yakun Zhu +4

Experienced physicians develop diagnostic expertise through clinical practice, acquiring not only disease knowledge but also the ability to differentiate confusable conditions. Cur…

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…

cs.CV2024

GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration

Sunan He, Yuxiang Nie, Hongmei Wang +21

Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of m…