no code implementations • 5 May 2023 • Yuanyuan Peng, Pengpeng Luan, Zixu Zhang
To enrich contextual information for the loss of scene information compensation, an attention fusion mechanism that combines the channel attention with spatial attention mechanisms constructed by Transformer is employed to extract various features of blood vessels from retinal fundus images.
no code implementations • 8 Apr 2023 • Meng Wang, Tian Lin, Lianyu Wang, Aidi Lin, Ke Zou, Xinxing Xu, Yi Zhou, Yuanyuan Peng, Qingquan Meng, Yiming Qian, Guoyao Deng, Zhiqun Wu, Junhong Chen, Jianhong Lin, Mingzhi Zhang, Weifang Zhu, Changqing Zhang, Daoqiang Zhang, Rick Siow Mong Goh, Yong liu, Chi Pui Pang, Xinjian Chen, Haoyu Chen, Huazhu Fu
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies.
no code implementations • 18 Jan 2023 • Yuanyuan Peng, Lin Pan, Pengpeng Luan, Hongbin Tu, Xiong Li
Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions.
no code implementations • 23 Jan 2022 • Yuanyuan Peng, Pengpeng Luan, Hongbin Tu, Xiong Li, Ping Zhou
Here, we adopt an ODoS filter by merging the orientation information and magnitude information to highlight structure features for fissure enhancement, which can effectively distinguish between pulmonary fissures and clutters.
no code implementations • 25 Oct 2021 • Yuanyuan Peng, Zixu Zhang, Hongbin Tu, Xiong Li
Results: The performance of the proposed method was validated in experiments with a publicly available dataset.