activity
20192023
most citedRemove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer

5 citations · 18 across the 12 of their papers we have counts for

collaborators

12 papers

eess.IV2023

FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound

Chaoyu Chen, Xin Yang, Yuhao Huang +11

Fetal pose estimation in 3D ultrasound (US) involves identifying a set of associated fetal anatomical landmarks. Its primary objective is to provide comprehensive information about…

cs.CV2023

Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays

Yiheng Xiong, Jingsong Liu, Kamilia Zaripova +3

The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating th…

cs.CV2022

Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling

Jiamin Liang, Xin Yang, Yuhao Huang +8

Ultrasound (US) is widely used for its advantages of real-time imaging, radiation-free and portability. In clinical practice, analysis and diagnosis often rely on US sequences rath…

cs.CV2022

Fine-grained Correlation Loss for Regression

Chaoyu Chen, Xin Yang, Ruobing Huang +10

Regression learning is classic and fundamental for medical image analysis. It provides the continuous mapping for many critical applications, like the attribute estimation, object…

cs.CV2021

Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound

Shuangchi He, Zehui Lin, Xin Yang +13

Standard plane recognition plays an important role in prenatal ultrasound (US) screening. Automatically recognizing the standard plane along with the corresponding anatomical struc…

cs.CV2021★ 4 cited

Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

Xin Yang, Yuhao Huang, Ruobing Huang +10

3D ultrasound (US) has become prevalent due to its rich spatial and diagnostic information not contained in 2D US. Moreover, 3D US can contain multiple standard planes (SPs) in one…

eess.IV2021

Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound

Xin Yang, Haoran Dou, Ruobing Huang +9

Accurate standard plane (SP) localization is the fundamental step for prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected to determine the clinical diagno…

eess.IV2021

Generalize Ultrasound Image Segmentation via Instant and Plug & Play Style Transfer

Zhendong Liu, Xiaoqiong Huang, Xin Yang +8

Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis. Retraining models leads to high latency and complex…

cs.CV2020★ 4 cited

Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

Yuhao Huang, Xin Yang, Rui Li +10

3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves effici…

cs.CV2020★ 2 cited

Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound

Chaoyu Chen, Xin Yang, Ruobing Huang +10

3D ultrasound (US) can facilitate detailed prenatal examinations for fetal growth monitoring. To analyze a 3D US volume, it is fundamental to identify anatomical landmarks of the e…

eess.IV2020★ 5 cited

Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer

Zhendong Liu, Xin Yang, Rui Gao +9

Deep Neural Networks (DNNs) suffer from the performance degradation when image appearance shift occurs, especially in ultrasound (US) image segmentation. In this paper, we propose…

eess.IV2019★ 3 cited

Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound

Haoran Dou, Xin Yang, Jikuan Qian +8

Standard plane localization is crucial for ultrasound (US) diagnosis. In prenatal US, dozens of standard planes are manually acquired with a 2D probe. It is time-consuming and oper…