activity
20202023
most citedLearn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

28 citations · 36 across the 6 of their papers we have counts for

collaborators

5 papers

cs.CV2022

HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images

Jikuan Qian, Rui Li, Xin Yang +8

Different from handcrafted features, deep neural networks can automatically learn task-specific features from data. Due to this data-driven nature, they have achieved remarkable su…

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.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…

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…