Nature communications · 2023
Uncertainty-inspired open set learning for retinal anomaly identification.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Wang Meng, Lin Tian, Wang Lianyu, Lin Aidi, Zou Ke, Xu Xinxing, Zhou Yi, Peng Yuanyuan, Meng Qingquan, Qian Yiming, Deng Guoyao, Wu Zhiqun, Chen Junhong, Lin Jianhong, Zhang Mingzhi, Zhu Weifang, Zhang Changqing, Zhang Daoqiang, Goh Rick Siow Mong, Liu Yong, Pang Chi Pui, Chen Xinjian, Chen Haoyu, Fu Huazhu
Аннотация
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. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.