Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

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.

Anomaly Classification Open Set Learning

Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images

no code implementations • 1 Dec 2022 • Meng Wang, Kai Yu, Chun-Mei Feng, Ke Zou, Yanyu Xu, Qingquan Meng, Rick Siow Mong Goh, Yong liu, Huazhu Fu

Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed.

Segmentation