no code implementations • 11 Aug 2021 • Shuangchi He, Zehui Lin, Xin Yang, Chaoyu Chen, Jian Wang, Xue Shuang, Ziwei Deng, Qin Liu, Yan Cao, Xiduo Lu, Ruobing Huang, Nishant Ravikumar, Alejandro Frangi, Yuanji Zhang, Yi Xiong, Dong Ni
In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously.
First, our proposed method is general and it can accurately localize multiple SPs in different challenging US datasets.
2D US has to perform scanning for each SP, which is time-consuming and operator-dependent.
Deep segmentation models that generalize to images with unknown appearance are important for real-world medical image analysis.
no code implementations • 30 Jul 2020 • Yuhao Huang, Xin Yang, Rui Li, Jikuan Qian, Xiaoqiong Huang, Wenlong Shi, Haoran Dou, Chaoyu Chen, Yuanji Zhang, Huanjia Luo, Alejandro Frangi, Yi Xiong, Dong Ni
In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously.
no code implementations • 1 Apr 2020 • Chaoyu Chen, Xin Yang, Ruobing Huang, Wenlong Shi, Shengfeng Liu, Mingrong Lin, Yuhao Huang, Yong Yang, Yuanji Zhang, Huanjia Luo, Yankai Huang, Yi Xiong, Dong Ni
The performance of the proposed framework is evaluated on a 3D US dataset to detect five key fetal facial landmarks.
In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs.
In this study, we propose a novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US.