no code implementations • CVPR 2021 • Daochang Liu, Qiyue Li, Tingting Jiang, Yizhou Wang, Rulin Miao, Fei Shan, Ziyu Li
In this paper, a unified multi-path framework for automatic surgical skill assessment is proposed, which takes care of multiple composing aspects of surgical skills, including surgical tool usage, intraoperative event pattern, and other skill proxies.
no code implementations • 7 Oct 2020 • Xuelin Qian, Huazhu Fu, Weiya Shi, Tao Chen, Yanwei Fu, Fei Shan, xiangyang xue
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic.
no code implementations • 27 Aug 2020 • Daochang Liu, Tingting Jiang, Yizhou Wang, Rulin Miao, Fei Shan, Ziyu Li
Then an objective and automated framework based on neural network is proposed to predict surgical skills through the proxy of COF.
1 code implementation • 27 Aug 2020 • Daochang Liu, Yuhui Wei, Tingting Jiang, Yizhou Wang, Rulin Miao, Fei Shan, Ziyu Li
In the experiments on the binary instrument segmentation task of the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset, the proposed method achieves 0. 71 IoU and 0. 81 Dice score without using a single manual annotation, which is promising to show the potential of unsupervised learning for surgical tool segmentation.
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 • 7 May 2020 • Xiaofeng Zhu, Bin Song, Feng Shi, Yanbo Chen, Rongyao Hu, Jiangzhang Gan, Wenhai Zhang, Man Li, Liye Wang, Yaozong Gao, Fei Shan, Dinggang Shen
To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives.
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 • 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).
no code implementations • 22 Mar 2020 • Feng Shi, Liming Xia, Fei Shan, Dijia Wu, Ying WEI, Huan Yuan, Huiting Jiang, Yaozong Gao, He Sui, Dinggang Shen
The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health.
1 code implementation • 10 Mar 2020 • Fei Shan, Yaozong Gao, Jun Wang, Weiya Shi, Nannan Shi, Miaofei Han, Zhong Xue, Dinggang Shen, Yuxin Shi
The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients.