most citedLearn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

28 citations · 36 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV20214 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…

cs.CV202128 cited

Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

Guang-Quan Zhou, Juzheng Miao, Xin Yang +7

Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have imp…

eess.IV2020

Contrastive Rendering for Ultrasound Image Segmentation

Haoming Li, Xin Yang, Jiamin Liang +12

Ultrasound (US) image segmentation embraced its significant improvement in deep learning era. However, the lack of sharp boundaries in US images still remains an inherent challenge…

cs.CV20204 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

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…