This study presents a novel framework for accurate prediction of missing teeth in different patterns, facilitating digital implant planning.
In this study, we develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation.
Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i. e., class centers) in a hyper-sphere manifold.
How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world.
no code implementations • 7 May 2020 • Liang Sun, Zhanhao Mo, Fuhua Yan, Liming Xia, Fei Shan, Zhongxiang Ding, Wei Shao, Feng Shi, Huan Yuan, Huiting Jiang, Dijia Wu, Ying WEI, Yaozong Gao, Wanchun Gao, He Sui, Daoqiang Zhang, Dinggang Shen
We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP).
no code implementations • 7 May 2020 • Donglin Di, Feng Shi, Fuhua Yan, Liming Xia, Zhanhao Mo, Zhongxiang Ding, Fei Shan, Shengrui Li, Ying WEI, Ying Shao, Miaofei Han, Yaozong Gao, He Sui, Yue Gao, Dinggang Shen
The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features.
no code implementations • 6 May 2020 • Xi Ouyang, Jiayu Huo, Liming Xia, Fei Shan, Jun Liu, Zhanhao Mo, Fuhua Yan, Zhongxiang Ding, Qi Yang, Bin Song, Feng Shi, Huan Yuan, Ying WEI, Xiaohuan Cao, Yaozong Gao, Dijia Wu, Qian Wang, Dinggang Shen
To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT).