no code implementations • 31 Jul 2024 • Mengtian Kang, Yansong Hu, Shuo Gao, Yuanyuan Liu, Hongbei Meng, Xuemeng Li, Xuhang Chen, Hubin Zhao, Jing Fu, Guohua Hu, Wei Wang, Yanning Dai, Arokia Nathan, Peter Smielewski, Ningli Wang, Shiming Li
In this study, we introduce a novel, high-accuracy method for quantitatively predicting the myopic trajectory and myopia risk in children using only fundus images and baseline refraction data.
no code implementations • 20 Apr 2024 • Yong liu, Mengtian Kang, Shuo Gao, Chi Zhang, Ying Liu, Shiming Li, Yue Qi, Arokia Nathan, Wenjun Xu, Chenyu Tang, Edoardo Occhipinti, Mayinuer Yusufu, Ningli Wang, Weiling Bai, Luigi Occhipinti
Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis.
no code implementations • 20 Apr 2024 • Jiaqi Wang, Mengtian Kang, Yong liu, Chi Zhang, Ying Liu, Shiming Li, Yue Qi, Wenjun Xu, Chenyu Tang, Edoardo Occhipinti, Mayinuer Yusufu, Ningli Wang, Weiling Bai, Shuo Gao, Luigi G. Occhipinti
Machine learning-based fundus image diagnosis technologies trigger worldwide interest owing to their benefits such as reducing medical resource power and providing objective evaluation results.
no code implementations • 30 Sep 2022 • Yifei Xu, Ye Guo, Wenjun Tang, Hongbin Sun, Shiming Li, Yue Dai
The problem of state estimations for electric distribution system is considered.
no code implementations • CVPR 2021 • Botong Wu, Sijie Ren, Jing Li, Xinwei Sun, Shiming Li, Yizhou Wang
In order to account for the degree of progression of the disease, we propose a temporal generative model to accurately generate the future image and compare it with the current one to get a residual image.
no code implementations • 8 Apr 2020 • Shasha Guo, Ziyang Kang, Lei Wang, Limeng Zhang, Xiaofan Chen, Shiming Li, Weixia Xu
Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers.
Emerging Technologies Signal Processing
no code implementations • 6 Apr 2020 • Shasha Guo, Lianhua Qu, Lei Wang, Shuo Tian, Shiming Li, Weixia Xu
To mitigate the difficulty in effectively dealing with huge input spaces of LSM, and to find that whether input reduction can enhance LSM performance, we explore several input patterns, namely fullscale, scanline, chessboard, and patch.