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.
In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH.
The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering.
In this paper, we present an integrated filter which comprises a weighted local guided image filter and a weighted spatiotemporal tree filter.